From 855fd2e44b3d76f9d58827af6cb7fca94fb9b473 Mon Sep 17 00:00:00 2001
From: Antonio Magherini <A.Magherini@student.tudelft.nl>
Date: Sun, 13 Aug 2023 17:45:12 +0200
Subject: [PATCH 1/2] Added references to Prob Design book

---
 book/_bibliography/references_pd.bib          | 318 ++++++++++++++++--
 ...rcise_Flood_Risk_Render_dont_execute.ipynb |  11 +-
 book/pd/reliability-component/overview.md     |   6 +-
 book/pd/reliability-system/overview.md        |  36 +-
 book/pd/risk-analysis/definition.md           |  31 +-
 book/pd/risk-analysis/steps.md                |  26 +-
 book/pd/risk-evaluation/cost-benefit.md       |  50 ++-
 book/pd/risk-evaluation/decision.md           |  12 +-
 book/pd/risk-evaluation/econ-optimization.md  |  27 +-
 book/pd/risk-evaluation/safety-standards.md   |  37 +-
 10 files changed, 445 insertions(+), 109 deletions(-)

diff --git a/book/_bibliography/references_pd.bib b/book/_bibliography/references_pd.bib
index 1b2bfdda..abed1c9d 100644
--- a/book/_bibliography/references_pd.bib
+++ b/book/_bibliography/references_pd.bib
@@ -1,12 +1,12 @@
 @book{adk2022,
   title={Structural and System Reliability},
-  author={Der Kiureghian, Armen},
+  author={Der Kiureghian, A.},
   year={2022},
   publisher={Cambridge University Press}
 }
 
 @article{baecher1982,
-   author = {Baecher, Gregory B},
+   author = {Baecher, Gregory B.},
    journal = {Engineering Foundations: Updating Subsurface Samplings of Soils and Rocks and their In-Situ Testing, Santa Barbara},
    title = {Statistical methods in site characterization},
    year = {1982},
@@ -14,61 +14,61 @@
 }
 
 @book{baecher2003,
-  title={Reliability and statistics in geotechnical engineering},
-  author={Baecher, Gregory B and Christian, John T},
+  title={Reliability and Statistics in Geotechnical Engineering},
+  author={Baecher, G.B. and Christian, J.T.},
   year={2003},
   publisher={John Wiley \& Sons}
 }
 
 @misc{baecher2021,
   title={IFCEE 2021: Karl Terzaghi Lecture: Greg Baecher: Geotechnical Systems, Uncertainty, and Risk},
-  author={Baecher, Gregory B},
+  author={Baecher, Gregory B.},
   howpublished={Presentation, YouTube: \url{https://www.youtube.com/watch?v=Y5w1p3uAe0I&ab_channel=Geo-InstituteofASCE}},
   year={2021}
 }
 
 @incollection{cardona2012,
-  title={Determinants of risk: exposure and vulnerability},
-  author={Cardona, Omar Dario and Van Aalst, Maarten K and Birkmann, J{\"o}rn and Fordham, Maureen and Mc Gregor, Glenn and Rosa, Perez and Pulwarty, Roger S and Schipper, E Lisa F and Sinh, Bach Tan and D{\'e}camps, Henri and others},
-  booktitle={Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change},
+  title={Determinants of Risk: Exposure and Vulnerability},
+  author={Cardona, O. D. and Van Aalst, M.K. and Birkmann, J. and Fordham, M. and Mc Gregor, G. and Rosa, P. and Pulwarty, R.S. and Schipper, E.L.F. and Sinh, B.T. and D{\'e}camps, H. and others},
+  booktitle={Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change},
   pages={65--108},
   year={2012},
   publisher={Cambridge University Press}
 }
 
 @book{ditlevsen1996,
-  title={Structural reliability methods},
-  author={Ditlevsen, Ove and Madsen, Henrik O},
+  title={Structural Reliability Methods},
+  author={Ditlevsen, O. and Madsen, H.O.},
   volume={178},
   year={1996},
   publisher={Wiley New York}
 }
 
 @article{Eijgenraam2006,
-   author = {C.J.J. Eijgenraam},
+   author = {Eijgenraam, C.J.J.},
    journal = {CPB discussion paper},
-   title = {Optimal safety standards for dike ring areas},
+   title = {Optimal safety standards for dike-ring areas},
    volume = {62},
    year = {2006},
 }
 
 @book{moss2020,
-  title={Applied civil engineering risk analysis},
-  author={Moss, Robb Eric S},
+  title={Applied Civil Engineering Risk Analysis},
+  author={Moss, R.E.S.},
   year={2020},
   publisher={Springer}
 }
 
 @article{vandantzig1956,
-  author          = {D. van Dantzig},
+  author          = {van Dantzig, D.},
   journal         = {Econometrica},
-  title           = {Economic decision problems for flood prevention},
+  title           = {Economic Decision Problems for Flood Prevention},
   volume          = {24},
   year            = {1956}
 }
 
 @article{vnk2014,
-  author          = {{Veiligheid Nederland in Kaart}},
+  author          = {VNK, {Veiligheid Nederland in Kaart}},
   title           = {De veiligheid van Nederland in kaart},
   year            = {2014},
   journal         = {Rijkswaterstaat},
@@ -78,21 +78,297 @@
 @article{vrijling1990,
   author          = {Vrijling, J.K. and van Beurden, J.A.},
   journal         = {Proceedings of the International Conference on Coastal Engineering},
-  title           = {Sealevel rise: a probabilistic design problem},
+  title           = {Sea Level Rise: A Probabilistic Design Problem},
   year            = {1990}
 }
 
 @article{vangelder1997,
-   author = {P.H.A.J.M. Van Gelder and J.K. Vrijling and K.A.H. Slijkhuis},
+   author = {Van Gelder, P.H.A.J.M. and Vrijling, J.K. and Slijkhuis, K.A.H.},
    journal = {Water for a Changing Global Community},
    pages = {554-559},
-   title = {Coping with uncertainty in the economical optimization of a dike design},
+   title = {Coping with Uncertainty in the Economical Optimization of a Dike Design},
    year = {1997},
 }
 
 @phdthesis{voorendt2017,
   title={Design principles of multifunctional flood defences},
-  author={Voorendt, Mark},
+  author={Voorendt, M.},
   school={Delft University of Technology, the Netherlands},
   year={2017}
+}
+
+@article{kaplan1981,
+   author = {Kaplan, S. and Garrick, B.J.},
+   journal = {Risk Analysis},
+   pages = {11-27},
+   title = {On The Quantitative Definition of Risk},
+   year = {1981},
+}
+
+@inproceedings{farmer1967,
+    title        = {Siting Criteria - A New Approach},
+    author       = {Farmer, F.R.},
+    year         = 1967,
+    month        = {April},
+    booktitle    = {Proceedings of IAEA Symposium on the Containment and Siting of Nuclear Power Plants},
+    publisher    = {International Atomic Energy Agency (IAEA)},
+    address      = {Vienna},
+    pages        = {303-324},
+}
+
+@book{kendall1977,
+  title={The Risks of Nuclear Power Reactors, A Review of the NRC Reactor Safety Study WASH-1400 (NUREG-75/014)},
+  author={Kendall, H.W. and Hubbard, R.B. and Minor, G.C. and Bryan, W.M. (Union of Concerned Scientists)},
+  year={1977},
+  publisher={Cambridge}
+}
+
+@article{vlek1996,
+   author = {Vlek, C.A.J.},
+   journal = {Risk Decision and Policy},
+   pages = {9-31},
+   title = {A multi-level, multi-stage and multi-attribute perspective on risk assessment, decision-making and risk control},
+   year = {1996},
+}
+
+@article{slovic1987,
+   author = {Slovic, P.},
+   journal = {Science},
+   pages = {280-285},
+   title = {Perception of Risk},
+   year = {1987},
+}
+
+@article{voortman2004,
+   author = {Voortman, H.G. and Vrijling, J.K.},
+   journal = {Heron},
+   pages = {75-94},
+   title = {Optimal design of flood defence systems in a changing climate},
+   year = {2004},
+}
+
+@article{apostolakis2004,
+   author = {Apostolakis, G.E.},
+   journal = {Risk Analysis},
+   pages = {515-520},
+   title = {How Useful Is Quantitative Risk Assessment?},
+   year = {2004},
+}
+
+@article{faber2003,
+title = {Risk Assessment for Civil Engineering Facilities: Critical Overview and Discussion},
+journal = {Reliability Engineering & System Safety},
+volume = {80},
+number = {2},
+pages = {173-184},
+year = {2003},
+issn = {0951-8320},
+doi = {https://doi.org/10.1016/S0951-8320(03)00027-9},
+url = {https://www.sciencedirect.com/science/article/pii/S0951832003000279},
+author = {Faber, M.H. and Stewart, M.G.},
+}
+
+@phdthesis{jongejan2008,
+  title={How safe is safe enough? The government's response to industrial and flood risks},
+  author={Jongejan, R.B.},
+  school={Delft University of Technology, the Netherlands},
+  year={2008}
+}
+
+@misc{CUR1997,
+  title={Kansen in de Civiele Techniek, Deel 1: Probabilistisch ontwerpen in theorie},
+  author={(Civieltechnisch Centrum Uitvoering research en Regelgeving), CUR},
+  howpublished={CUR rapport 190},
+  year={1997}
+}
+
+@misc{CIB2001,
+  title={A framework for risk management and risk communications},
+  author={Workgroup 32, CIB},
+  howpublished={CIB report 259},
+  year={2001}
+}
+
+@article{bea1998,
+   author = {Bea, R.G.},
+   journal = {Reliability Engineering & System Safety},
+   pages = {109-126},
+   title = {Human and Organization Factors: Engineering Operating Safety Into Offshore Structures},
+   year = {1998},
+}
+
+@misc{eurocode2001,
+  title={Eurocode - Basis of Structural Design},
+  author={EN 1990:2002 E, Eurocode},
+  howpublished={CEN},
+  year={2001}
+}
+
+@book{vrouwenvelder1996,
+   author = {Vrouwenvelder, A.C.W.M. and Vrijling, J.K},
+   title = {Collegedictaat Probabilistisch ontwerpen b3},
+   year = {1996},
+   publisher={TU Delft}
+}
+
+@article{starr1967,
+   author = {Starr, C.},
+   journal = {Science},
+   pages = {1232-1238},
+   title = {Social benefit versus technological risk},
+   year = {1967},
+}
+
+@inproceedings{taerwe1986,
+    title        = {Mastering Global Quality by an Interactive Concept},
+    author       = {Taerwe, L.},
+    year         = 1986,
+    booktitle    = {Proceedings IABSE Symposium Tokyo 1986: Safety and Quality Assurance of Civil Engineering Structures},
+    pages        = {317-325},
+}
+
+@book{raiffa1961,
+   author = {Raiffa, H. and Schlaifer, R.},
+   title = {Applied Statistical Decision Theory},
+   year = {1961},
+   publisher={Harvard University press},
+   address = {Cambridge, Massachusetts, United States},
+}
+
+@book{benjamin1970,
+   author = {Benjamin, J.R. and Cornell, C.A.},
+   title = {Probability, Statistics and Decision for Civil Engineers},
+   year = {1970},
+   publisher={McGraw Hill},
+}
+
+@inproceedings{don2003,
+   author = {Don, F.H. and Stolwijk, H.J.J.},
+   booktitle = {CUR (2003) 50 jaar na stormvloed 1953, terugblik en toekomst},
+   title = {Investeren in veiligheid: de watersnoodramp van 1953 en de kosten en baten van het deltaplan},
+   pages={79-98},
+   year = {2003},
+   howpublished={CUR rapport 212},
+}
+
+@inproceedings{vrijling2000,
+  title={An Analysis of the Valuation of a Human Life},
+  author={Vrijling, J.K. and Gelder, P.H.A.J.M. van},
+  booktitle={Foresight and Precaution: Proceedings of ESREL 2000, SARS and SRA-Europe Annual Conference. Edited by M.P. Cottam, D.W. Harvey, R.P. Pape and J. Tait},
+  pages={197-200},
+  volume={1},
+  year={2000},
+  address={Edinburgh, Scotland}
+}
+
+@article{jongejan2005,
+   author = {Jongejan, R.B. and Jonkman, S.N. and Vrijling, J.K.},
+   journal = {ESREL},
+   title = {Methods for the Valuation of Human Life},
+   year = {2005},
+}
+
+@misc{SWOV2012,
+  title={Waardering van immateriële kosten van verkeersdoden},
+  author={SWOV},
+  howpublished={Factsheet, \url{http://www.swov.nl/rapport/Factsheets/NL/Factsheet_Immateriele_kosten.pdf}},
+  year={2012},
+}
+
+@inproceedings{jeuken2013,
+author = {Jeuken, A. and Kind, J. and Slootjes, N. and Gauderis, J. and Vos, R.},
+year = {2013},
+month = {01},
+pages = {},
+title = {Cost-benefit Analysis of Flood Risk Management Strategies for the Rhine-Meuse Delta},
+}
+
+@article{tengs1995,
+   author = {Tengs, T.O. and Adams, M.E. and Pliskin, J.S. and Gelb Safran, D. and Siegel, J.E. and Weinstein, M.C. and Graham, J.D.},
+   journal = {Risk Analysis},
+   volume={15},
+   number={3},
+   pages={369-390},
+   title = {Five-hundred Live Saving Interventions and Their Cost Effectiveness},
+   year = {1995},
+}
+
+@misc{TAW1985,
+  title={Some Considerations of an Acceptable Level of Risk in the Netherlands},
+  author={TAW, (Technical Advisory Committee on Water Defences)},
+  howpublished={Report by TAW workgroup 10 'Probabilistic methods'}, 
+  year={1985},
+}
+
+@article{vrijling1995,
+   author = {Vrijling, J.K. and Hengel, W. van and Houben, R.J.},
+   journal = {Journal of Hazardous Materials},
+   volume={43},
+   pages = {245-261},
+   title = {A Framework for Risk Evaluation},
+   year = {1995},
+}
+
+@article{vrijling1998,
+   author = {Vrijling, J.K. and Hengel, W. van and Houben, R.J.},
+   journal = {Reliability Engineering and System Safety},
+   volume={59},
+   pages = {141-150},
+   title = {Acceptable Risk as a Basis for Design},
+   year = {1998},
+}
+
+@article{jonkman2003,
+   author = {Jonkman, S.N. and van Gelder, P.H.A.J.M. and Vrijling, J.K.},
+   journal = {Journal of Hazardous Materials},
+   volume={A99},
+   pages = {1-30},
+   title = {An Overview of Quantitative Risk Measures for Loss of Life and Economic Damage},
+   year = {2003},
+}
+
+@article{bottelberghs2000,
+   author = {Bottelberghs, P.H.},
+   journal = {Journal of Hazardous Materials},
+   volume={71},
+   pages = {117-123},
+   title = {Risk Analysis and Safety Policy Developments in the Netherlands},
+   year = {2000},
+}
+
+@misc{CUR2015,
+  title={Kansen in de Civiele Techniek, Deel 1: Probabilistisch ontwerpen in theorie},
+  author={CUR, (Civieltechnisch Centrum Uitvoering research en Regelgeving)},
+  howpublished={CUR rapport 190},
+  year={2015}
+}
+
+@article{vrijling2001,
+title = {Probabilistic Design of Water Defense Systems in The Netherlands},
+journal = {Reliability Engineering & System Safety},
+volume = {74},
+number = {3},
+pages = {337-344},
+year = {2001},
+issn = {0951-8320},
+doi = {https://doi.org/10.1016/S0951-8320(01)00082-5},
+url = {https://www.sciencedirect.com/science/article/pii/S0951832001000825},
+author = {Vrijling, J.K.},
+}
+
+@misc{delta2014,
+  title={Deltaprogramma Veiligheid. Synthesedocument Veiligheid},
+  author={Deltaprogramma},
+  howpublished={Achtergronddocument B1},
+  year={2014}
+}
+
+@article{sitar1987,
+title = {First-order Reliability Approach to Stochastic Analysis of Subsurface Flow and Contaminant Transport},
+journal = {Water Resources Research},
+volume = {23},
+number = {5},
+pages = {794-804},
+year = {1987},
+author = {Sitar, N. and Cawlfield, J. D. and Der Kiureghian, A.},
 }
\ No newline at end of file
diff --git a/book/pd/notebooks/flood-risk/Exercise_Flood_Risk_Render_dont_execute.ipynb b/book/pd/notebooks/flood-risk/Exercise_Flood_Risk_Render_dont_execute.ipynb
index 09ce9bd3..410084f2 100644
--- a/book/pd/notebooks/flood-risk/Exercise_Flood_Risk_Render_dont_execute.ipynb
+++ b/book/pd/notebooks/flood-risk/Exercise_Flood_Risk_Render_dont_execute.ipynb
@@ -27,7 +27,7 @@
    "id": "6897cbb0-6e59-4ed3-a346-ae684e115bbc",
    "metadata": {},
    "source": [
-    "The overall objective of this assignment is to make a step from the simple FN/FD curves we have in the book to work on something 'real' and to make 'real' engineering decisions about how to modify a dike ring to meet safety standards. This exercise is based on a risk assessment of the Netherlands from around 10 years ago called VNK (veiligheid Nederland in kaart, or mapping Dutch (flood) safety. It was done in the runup to changing the safety standards in 2017. You can find more about the project [here](https://www.helpdeskwater.nl/onderwerpen/waterveiligheid/programma-projecten/veiligheid-nederland/publicaties/), and a report specifically about the case [here](https://www.helpdeskwater.nl/onderwerpen/waterveiligheid/programma-projecten/veiligheid-nederland/publicaties/dijkringrapporten/dijkringrapporten/fase-1a/14-zuid-holland/). The first website has some English reports, but unfortunately the Dike Ring 14 report is only in Dutch.\n",
+    "The overall objective of this assignment is to make a step from the simple FN/FD curves we have in the book to work on something 'real' and to make 'real' engineering decisions about how to modify a dike ring to meet safety standards. This exercise is based on a risk assessment of the Netherlands from around 10 years ago called [VNK (2014)](http://resolver.tudelft.nl/uuid:52035faa-43ab-4dd0-a5b0-099119085356) (Veiligheid Nederland in Kaart, or mapping Dutch (flood) safety). It was done in the runup to changing the safety standards in 2017. You can find more about the project [here](https://www.helpdeskwater.nl/onderwerpen/waterveiligheid/programma-projecten/veiligheid-nederland/publicaties/), and a report specifically about the case [here](https://www.helpdeskwater.nl/onderwerpen/waterveiligheid/programma-projecten/veiligheid-nederland/publicaties/dijkringrapporten/dijkringrapporten/fase-1a/14-zuid-holland/). The first website has some English reports, but unfortunately the Dike Ring 14 report is only in Dutch.\n",
     "\n",
     "*First the case is introduced, then some examples using the pre-prepared code are given to show you how to use it. Then you are asked to use the code to decide which repairs to specific dike segments should be done to see if you can meet the safety criteria!*\n",
     "\n",
@@ -64,7 +64,10 @@
     "e. repeat this for many breach locations\n",
     "    \n",
     "As such, each scenario returns a finite number of fatalities or damage, which is why the distribution is discrete. Of course, we need to also quantify the probability of each scenario. Although it sounds complex, it is actually simple to represent using conditional probability if we can assume:\n",
-    "$$\\textrm{P}(x_i)=\\textrm{P}(x_i|D,W,E)\\cdot\\textrm{P}(D)\\cdot\\textrm{P}(W)\\cdot\\textrm{P}(E)$$\n",
+    "\n",
+    "$$ \n",
+    "\\textrm{P}(x_i)=\\textrm{P}(x_i|D,W,E)\\cdot\\textrm{P}(D)\\cdot\\textrm{P}(W)\\cdot\\textrm{P}(E)\n",
+    "$$\n",
     "\n",
     "where $x_i$ is our risk metric of choice (i.e., economic damage or fatalities) and $D$, $W$ and $E$ represnt water depth, warning and evacuation status, respectively. Given the scenario-based approach where a fixed value is determined for $x_i$, it is clear that this type of analysis creates a descrete random variable. Furthermore, the equation above indicates that single values of $D$, $W$ and $E$ are used, but in theory one could integrate over each of these variables.\n",
     "    \n",
@@ -649,7 +652,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -688,7 +691,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
diff --git a/book/pd/reliability-component/overview.md b/book/pd/reliability-component/overview.md
index 4daf8d1b..ee7f11ba 100644
--- a/book/pd/reliability-component/overview.md
+++ b/book/pd/reliability-component/overview.md
@@ -7,9 +7,11 @@ This Section gives a brief introduction to some of the mathematical foundations
 
 In short, a component reliability analysis evaluates the reliability of a particular engineered object, where reliability is the complement of failure probability, $p_f$. The analysis is performed by defining a function of random variables, $g_X(x)$, and mathematically specifying a region of interest, $\Omega$, often called the failure region. The function of random variables implies a multivariate probability density function, $f_X(x)$, where integrating over the failure region $\Omega$ gives $p_f$. 
 
-$$
+```{math}
+:label: failure_probability
+
 p_f=\int_\Omega f_X(x)\:\mathrm{d}x
-$$
+```
 
 ## A Simple Case
 
diff --git a/book/pd/reliability-system/overview.md b/book/pd/reliability-system/overview.md
index a628aae1..98dd2e01 100644
--- a/book/pd/reliability-system/overview.md
+++ b/book/pd/reliability-system/overview.md
@@ -49,15 +49,19 @@ Equations for computing parallel and series system reliability are described her
 
 A parallel system is often described as redundant since *all* components must fail to cause system failure. This is also simple to calculate as it can be described mathematically as the joint failure probability, or intersection, of all components:
 
-$$
+```{math}
+:label: parallel_system
+
 p_{f,sys} = P(F_1 \cap F_2 \cap ... \cap F_n)
-$$
+```
 
 If the components are independent, this becomes
 
-$$
+```{math}
+:label: parallel_system_sum
+
 p_{f,sys} = \prod_i^n P(F_i)
-$$
+```
 
 Although we will not quantify the effect of dependence in depth here, it is easy to see semi-quantitatively that positive dependence increases the intersection probability, and therefore also increases $p_{f,sys}$. Negative dependence has the opposite effect. In fact, if parallel components are mutually exclusive the system is guaranteed to survive. The case of negative dependence is trivial in engineering reliability computation, but something that is used to motivate the incorporation of resilience or robustness in our risk reduction decisions.
 
@@ -65,27 +69,35 @@ Although we will not quantify the effect of dependence in depth here, it is easy
 
 A series system fails if *any* component fails, which is 
 
-$$
+```{math}
+:label: series_system
+
 p_{f,sys} = P(F_1 \cup F_2 \cup ... \cup F_n)
-$$
+```
 
 For two components this can easily be re-written as 
 
-$$
+```{math}
+:label: sys_p_series
+
 p_{f,sys} = P(F_1) + P(F_2) - P(F_1 \cap F_2)
-$$ (sys_p_series)
+```
 
 Although it is straightforward to write out terms for 3 or more components, it is easier to re-write the union formulation as the complement of joint *survival* of the system:
 
-$$
+```{math}
+:label: sys_p_series_3 
+
 p_{f,sys} = 1 - P(\bar{F}_1 \cap \bar{F}_2 \cap ... \cap \bar{F}_n)
-$$
+```
 
 As $\bar{F}_i=1-F_i$, we arrive at the following simple relationship:
 
-$$
+```{math}
+:label: series_system_sum 
+
 p_{f,sys} = 1 - \prod_i^n \left(1-P(F_i)\right)
-$$
+```
 
 The influence of dependence on a series system is most easily understood through consideration of {eq}`sys_p_series`, which allows a direct analogy to the parallel case through the intersection term. With positive dependence, the system failure probability decreases 
 
diff --git a/book/pd/risk-analysis/definition.md b/book/pd/risk-analysis/definition.md
index 43e63e4f..630cbb05 100644
--- a/book/pd/risk-analysis/definition.md
+++ b/book/pd/risk-analysis/definition.md
@@ -10,14 +10,23 @@ An often-used definition considers risk as an expected value:
 
 The unit of risk now depends on the units of probability and consequences, where probability of an event is generally expressed as the probability per unit time, for example per year. The consequences of an undesired event often measure a diverse array of damages, such as material, ecological, injuries and fatalities. In many applications engineering consequences are expressed by means of a monetary value, in which case the unit of the risk (or expected value, $E(d)$) then becomes € per year. For a case with one event scenario $i$ with probability $p_{i}$ it yields:
 
- $$
- E(d_i) = p_{i} \cdot d_{i} 
- $$ 
+```{math}
+:label: single_risk
+
+E(d_i) = p_{i} \cdot d_{i} 
+``` 
  
-A more general definition of risk has been given by Kaplan and Garrick (1981):  
+A more general definition of risk has been given by {cite:t}`kaplan1981`:  
 
->Risk is a set of scenarios, $s_{i}$, each of which has a probability, $p_{i}$, and a consequence, $d_{i}$.
+>Risk $R$ is the set of triplets $R=\{<s_{i}, p_{i}, x_{i}>\}, \hspace{10mm} {i=1,2 ,..., N}$, 
+\
+where $s_{i}$ is a scenario identification or description;
+\
+$\hspace{12mm} p_{i}$ is the probability of that scenario; and
+\
+$\hspace{12mm} x_{i}$ is the consequence or evaluation evaluation measure of that scenario, i.e., the measure of damage.
 
+In our case, $x_{i}=d_{i}$.
 
 This definition allows the use of various so-called risk metrics (or risk measures) to quantify or depict risk. The expected value of the damage for a set of multiple discrete scenarios $i=1,....,n$ , can be expressed as:
 
@@ -40,7 +49,7 @@ name: FN-curve-simple
 FN curve, showing the probability of exceedance of a certain number of fatalities N on Log-Log scale.
 ```
 
-The FN curve was originally introduced in the 1960's for the assessment of risks in the nuclear industry (Farmer, 1967; Kendall et al., 1977) and is now used to display and limit risks in a wide variety of industries around the world. It is an extremely useful way to quantitatively compare risk associated with a broad range of scenarios, and to make decisions. A famous example of this is shown in {numref}`risk-curve-baecher`, which compares the risk estimated for a wide variety of engineering infrastructure. This figure is described further in the {ref}`risk_curve` Section, and also illustrates the concept of acceptable risk, which is discussed in the Section on {ref}`safety_standards`. In short, it allows one to begin answering the question 'how safe is safe enough?'
+The FN curve was originally introduced in the 1960's for the assessment of risks in the nuclear industry ({cite:t}`farmer1967`; {cite:t}`kendall1977`) and is now used to display and limit risks in a wide variety of industries around the world. It is an extremely useful way to quantitatively compare risk associated with a broad range of scenarios, and to make decisions. A famous example of this is shown in {numref}`risk-curve-baecher`, which compares the risk estimated for a wide variety of engineering infrastructure. This figure is described further in the {ref}`risk_curve` Section, and also illustrates the concept of acceptable risk, which is discussed in the Section on {ref}`safety_standards`. In short, it allows one to begin answering the question 'how safe is safe enough?'
 
 ```{figure} ../../figures/pd/risk-curve-baecher.PNG
 ---
@@ -83,11 +92,13 @@ where $N_i$, $E_j$ and $H_k$ are the vulnerability (fatalities), exposure and ha
 
 This book uses Equation {eq}`eq_risk_definition` as the primary definition of risk. However, it is useful to highlight some risk concepts used in other domains.
 
-Within economics, risk is generally associated with a deviation from the expected return or the probability of loss. In social sciences risk is often considered as a contextual notion or social construct. Vlek (1996) has summarized 11 formal definitions used in social sciences, see Table {numref}`risk_definitions`. In some of these definitions (e.g. numbers 2 and 4) the perceived seriousness of the undesired consequences plays an important role. Examples of other, more informal risk definitions used in psychology are “the lack of perceived controllability”, “set of possible negative consequences” and “fear of loss” (Vlek, 1996). 
+Within economics, risk is generally associated with a deviation from the expected return or the probability of loss. In social sciences risk is often considered as a contextual notion or social construct. {cite:t}`vlek1996` has summarized 11 formal definitions used in social sciences, see Table {numref}`risk_definitions`. In some of these definitions (e.g. numbers 2 and 4) the perceived seriousness of the undesired consequences plays an important role. Examples of other, more informal risk definitions used in psychology are, according to {cite:t}`vlek1996`,
 
-Substantial research has also focused on factors that determine the perception of risk (e.g. Slovic, 1987, Vlek, 1996) including: degree of damage, controllability of and familiarity with hazards, extent of benefits from an activity, and voluntariness of exposure. 
+>“set of possible negative consequences”, “the lack of perceived controllability”, and “fear of loss”.  
 
-```{list-table} Formal definitions of risk used in social sciences (Vlek, 1996)
+Substantial research has also focused on factors that determine the perception of risk (e.g. {cite:t}`slovic1987`, {cite:t}`vlek1996`) including: degree of damage, controllability of and familiarity with hazards, extent of benefits from an activity, and voluntariness of exposure. 
+
+:::{list-table} Formal definitions of risk used in social sciences {cite:p}`vlek1996`
 :header-rows: 1
 :name: risk_definitions
 
@@ -115,5 +126,5 @@ Substantial research has also focused on factors that determine the perception o
   - Weighted combination of various parameters of the probability distribution of all possible consequences
 * - 11
   - Weight of possible undesired consequences (‘loss’) relative to comparable possible desired consequences
-```
+:::
 
diff --git a/book/pd/risk-analysis/steps.md b/book/pd/risk-analysis/steps.md
index da196736..53963b75 100644
--- a/book/pd/risk-analysis/steps.md
+++ b/book/pd/risk-analysis/steps.md
@@ -4,9 +4,9 @@
 
 The previous section made it clear that risk is a function of probabilities and consequences. Risk analysis, therefore, consists of an analysis of probabilities and consequences associated with undesired events in a given system. Alternative terms used in literature are risk assessment and quantitative risk analysis (QRA). 
 
-A risk analysis is carried out because involved parties (e.g. designers, managers, decision makers) want to identify and evaluate the risks and decide on their acceptability. Outcomes of risk analysis can be used in the design process to decide on the required safety levels of new systems (e.g. a new tunnel) or to support decisions on the acceptability of safety levels and the need for measures in existing systems (e.g. a flood defence system). A quantitative measure of some form is needed to transfer decisions on acceptable safety into a technical domain (Voortman, 2004), such as the height of a flood defence or the strength of a building. In addition, risk analysis can be used to analyse the effectiveness of risk reduction measures, including management and maintenance strategies. Whereas the primary goal of a risk analysis is often to support rational decision-making regarding risk-bearing activities (Apostolakis, 2004), it also provides insight in the way a system may fail. As such, it can also serve as a tool of communication and management. Insights drawn from a risk analysis can be used to optimize system design and management and often there is a direct link to quality assurance. 
+A risk analysis is carried out because involved parties (e.g. designers, managers, decision makers) want to identify and evaluate the risks and decide on their acceptability. Outcomes of risk analysis can be used in the design process to decide on the required safety levels of new systems (e.g. a new tunnel) or to support decisions on the acceptability of safety levels and the need for measures in existing systems (e.g. a flood defence system). A quantitative measure of some form is needed to transfer decisions on acceptable safety into a technical domain {cite:p}`voortman2004`, such as the height of a flood defence or the strength of a building. In addition, risk analysis can be used to analyse the effectiveness of risk reduction measures, including management and maintenance strategies. Whereas the primary goal of a risk analysis is often to support rational decision-making regarding risk-bearing activities {cite:p}`apostolakis2004`, it also provides insight in the way a system may fail. As such, it can also serve as a tool of communication and management. Insights drawn from a risk analysis can be used to optimize system design and management and often there is a direct link to quality assurance. 
 
-In general, the following elements can be identified within a risk analysis, (based on: CUR, 1997; CIB, 2001; Faber and Stewart, 2003; Jongejan, 2008): 
+In general, the following elements can be identified within a risk analysis, (based on: {cite:t}`CUR1997`, {cite:t}`CIB2001`, {cite:t}`faber2003`, {cite:t}`jongejan2008`): 
 
 1. System definition and setting the scope and objectives of the analysis;
 2. Qualitative analysis of undesired events; 
@@ -27,13 +27,13 @@ Schematic view of steps in risk analysis (risk assessment) and risk management.
 
 This step entails the definition and description of the system as well as the scope and objectives of the analysis. Usually a system is divided into components and subsystems, which can be schematised graphically. Further information on the decomposition and analysis of system is provided in the {ref}`rel_sys` Chapter of this book, and illustrated through the example exercises.
 
-A system can be represented in terms of physical components, organizations and users, and an external environment. In order to analyse failure and risks, not only physical components, but also organizations and operators and users need to be considered (see e.g. Bea, 1998), as different groups of organizations and persons will be involved in different roles. Consider the aviation industry, where professionals may be responsible for operation and management of the system (e.g. the pilots and crew in an aircraft), but important roles are also played by the users of a system (passengers) and external parties (people living near the airport exposed to risk and noise). Each group has a different relationship and attitude towards the risk, which could affect its acceptability. For example, a higher risk might be acceptable for pilots and crews in an aircraft (who derive a direct benefit from flying) than for regular passengers. Finally, the external environment (e.g. wind or runway conditions) will determine the loads on the system and affect potential failure mechanisms.
+A system can be represented in terms of physical components, organizations and users, and an external environment. In order to analyse failure and risks, not only physical components, but also organizations and operators and users need to be considered (see e.g. {cite:t}`bea1998`), as different groups of organizations and persons will be involved in different roles. Consider the aviation industry, where professionals may be responsible for operation and management of the system (e.g. the pilots and crew in an aircraft), but important roles are also played by the users of a system (passengers) and external parties (people living near the airport exposed to risk and noise). Each group has a different relationship and attitude towards the risk, which could affect its acceptability. For example, a higher risk might be acceptable for pilots and crews in an aircraft (who derive a direct benefit from flying) than for regular passengers. Finally, the external environment (e.g. wind or runway conditions) will determine the loads on the system and affect potential failure mechanisms.
 
 ## 2. Qualitative analysis
 
 In this step, potential hazards, undesired events, failure mechanisms and scenarios are identified and described. An important goal of this phase is to gain insight, as complete as possible, into all possible undesired events and their consequences. For most systems, multiple undesired events can be distinguished. For example, two events with different impacts that can both lead to flooding of a polder are 1) the inflow of large amounts of water due to a dike failure; 2) the inflow of smaller amounts of water when a sluice gate is not closed. 
 
-Failure occurs when a system no longer fulfils one or more desired functions, where the failed state can be reached through different failure mechanisms (or failure modes). For example, a dike can fail due to overtopping, but also due to geotechnical failure mechanisms such as instability or piping. A **limit state** is a condition of a structure beyond which it no longer fulfils the relevant design criteria (Eurocode, 2001), of which two types are distinguished in practice: serviceability and ultimate limit states (SLS and ULS, respectively).  
+Failure occurs when a system no longer fulfils one or more desired functions, where the failed state can be reached through different failure mechanisms (or failure modes). For example, a dike can fail due to overtopping, but also due to geotechnical failure mechanisms such as instability or piping. A **limit state** is a condition of a structure beyond which it no longer fulfils the relevant design criteria {cite:p}`eurocode2001`, of which two types are distinguished in practice: serviceability and ultimate limit states (SLS and ULS, respectively).  
 
 - **Ultimate limit state (ULS)**: failure or collapse of a system or structure occurs, for example, the breakwaters of a harbour entrance are destroyed as a result of extreme conditions. An example from structural engineering is the collapse of a roof of a building.
 - **Serviceability limit state (SLS)**: exceedance leads to temporary and/or partial failure. For example, inability to use the harbour due to waves that are (temporarily) too high. Another example could be too much vibration of a structure, such that users experience discomfort. 
@@ -48,7 +48,7 @@ The probabilities and consequences of the undesired events identified in step 2
 
 ### Probability
 
-In simple cases the probability can be computed directly, but generally the undesired event must broken down into multiple steps or processes to facilitate the computation of probability, which are then recombined using integration or the total probability theorem. Such an approach results in systems of systems, each of which is composed of individual *components* (or elements0, each of which is typically evaluated using the limit state concepts defined above. Often this the probability of a limit state being exceeded is referred to as the probability of failure, which of course reflects the component, not necessarily the system. Techniques for computing the probability of failure are covered in the {ref}`rel_comp` and {ref}`rel_sys` Chapters.
+In simple cases the probability can be computed directly, but generally the undesired event must broken down into multiple steps or processes to facilitate the computation of probability, which are then recombined using integration or the total probability theorem. Such an approach results in systems of systems, each of which is composed of individual *components* (or elements, each of which is typically evaluated using the limit state concepts defined above. Often this the probability of a limit state being exceeded is referred to as the probability of failure, which of course reflects the component, not necessarily the system. Techniques for computing the probability of failure are covered in the {ref}`rel_comp` and {ref}`rel_sys` Chapters.
 
 Whereas the most critical aspect of the previous step is identifying the most important failure modes, in the quantitative analysis it is critical to precisely define the quantity of interest, as ambiguity can lead to misunderstandings and incorrect assessment of risk. Often such ambiguities have a direct relationship with the conditional terms used inthe probability computations, which imply specific statements about dependence (or independence). Consider the previous example of river level exceeding the dike height $P(h_w>h_{dike})$: the probability can be significantly different if a design lifetime of one year or fifty years is considered. In addition, the time of year may also play a role as different types of floods may occur. If the seasonal probability of flooding is binary and mutually exclusive with wet and dry season each lasting 6 months of the year, the following expression holds (all values computed an a *per year* basis):
 
@@ -68,13 +68,17 @@ After failure has been defined, consequences of the events are quantified. First
 
 The probability of damage can now be computed by combining these terms:
 
-$$P(D)=P(E_{1})P(E_{2}|E_{1})P(D|E_{1}\cap E_{2})$$                        
+```{math}
+:label: probability_of_damage
+
+P(D)=P(E_{1})P(E_{2}|E_{1})P(D|E_{1}\cap E_{2})
+ ```                      
 
 which is analagous to the hazard, vulnerability and exposure formulation of risk introduced in the previous Section.
 
 Multiple types of consequences can be caused by one disaster, which are illustrated in {numref}`risk_damage_types` for the failure of a flood protection system. The damage is divided into tangible and intangible damage, depending on whether or not the losses can be assessed in monetary values. Another distinction is made between the direct damage, caused by physical effects of the event, and damages occurring outside the directly exposed area. The latter occurs when companies outside an impacted area experience damages, due to loss of demand from customers in the flooded area. In a risk analysis it is desirable to take into account a complete set of impacts; unfortunately many of the items from the table cannot be easily quantified, if at all, thus the quantitative analysis and risk evaluation are often focused on economic damages and life loss.
 
-```{list-table} General classification of damages, based on (Vrouwenvelder and Vrijling, 1996)
+:::{list-table} General classification of damages, based on {cite:t}`vrouwenvelder1996`.
 :header-rows: 1
 :name: risk_damage_types
 
@@ -123,11 +127,11 @@ Multiple types of consequences can be caused by one disaster, which are illustra
 * - 
   - Temporary housing of evacuees
   - 
-```
+:::
 
 ## 4. Risk Evaluation
 
-In the risk evaluation phase a decision is made whether the risk is acceptable or not and whether risk reduction measures should be implemented. In other words, a direct answer is sought to the question “how safe is safe enough?” (Starr, 1967). Results of the quantitative analysis provide input for risk evaluation and decision making, and different quantitative approaches can be used to support risk evaluation, which are outlined here and introduced formally in the {ref}`risk_eval` Chapter.
+In the risk evaluation phase a decision is made whether the risk is acceptable or not and whether risk reduction measures should be implemented. In other words, a direct answer is sought to the question “how safe is safe enough?” {cite:p}`starr1967`. Results of the quantitative analysis provide input for risk evaluation and decision making, and different quantitative approaches can be used to support risk evaluation, which are outlined here and introduced formally in the {ref}`risk_eval` Chapter.
 
 **Decision Analysis**: Also called 'decision making under uncertainty,' this approach aids the decision-making process by recording different possible outcomes along with the associated risks, costs and benefits, leading to the optimal selection from a number of alternatives. Often visual tools are used such as a risk matrix or decision tree.
 
@@ -135,10 +139,10 @@ In the risk evaluation phase a decision is made whether the risk is acceptable o
 
 **Safety Standards**: risk is compared with predetermined safety standards to directly determin acceptability. Such standards are typically imposed by government organizations or standards of practice and often focus on loss of life as the primary risk metric.
 
-Given the nature of the key question "how safe is safe enough?", several political, psychological and social processes play a role in the evaluation of risk---in other words: risk evaluation is not purely a technical process, but involves many subjective elements and decisions. One difficulty facing regulators is that preferences and risk attitudes within society may diverge and that costs and benefits may not be distributed evenly, and that a single, collective decision has to be based on strongly divergent individual preferences. In practice, this implies that the establishment of collective decision making procedures is inevitably a political process. This ambiguity can also be found in the numerous interpretations of “the” precautionary principle, which is interpreted by some as a decision making criterion that requires proof of harmlessness (a scientific impossibility), whereas it is seen by others as a decision making procedure that puts emphasis on dialogue and stakeholder involvement, (e.g. Jongejan, 2008).
+Given the nature of the key question "how safe is safe enough?", several political, psychological and social processes play a role in the evaluation of risk---in other words: risk evaluation is not purely a technical process, but involves many subjective elements and decisions. One difficulty facing regulators is that preferences and risk attitudes within society may diverge and that costs and benefits may not be distributed evenly, and that a single, collective decision has to be based on strongly divergent individual preferences. In practice, this implies that the establishment of collective decision making procedures is inevitably a political process. This ambiguity can also be found in the numerous interpretations of “the” precautionary principle, which is interpreted by some as a decision making criterion that requires proof of harmlessness (a scientific impossibility), whereas it is seen by others as a decision making procedure that puts emphasis on dialogue and stakeholder involvement, e.g. {cite:t}`jongejan2008`.
 
 ## 5. Risk Reduction and Risk Control
 
 If the risks evaluated in first four steps of a risk analysis are considered unacceptable, several forms of risk reduction can be implemented, such as changes to the engineered system, or changes to its organization and management. It can be helpful to determine how risk can be controlled, for example by monitoring, inspection or maintenance.
 
-Post-accident analysis indicates that human and organizational errors are still a major cause of failure in civil engineering systems, and it seems that the only suitable way to reduce human errors is by the incorporation of sufficient control in different phases of the construction process (Taerwe, 1986), and by a thorough education of all personnel involved. Therefore, an extensive interaction between the safety methodology and quality management is a necessity in order to guarantee the safety of infrastructure.
\ No newline at end of file
+Post-accident analysis indicates that human and organizational errors are still a major cause of failure in civil engineering systems, and it seems that the only suitable way to reduce human errors is by the incorporation of sufficient control in different phases of the construction process {cite:p}`taerwe1986`, and by a thorough education of all personnel involved. Therefore, an extensive interaction between the safety methodology and quality management is a necessity in order to guarantee the safety of infrastructure.
\ No newline at end of file
diff --git a/book/pd/risk-evaluation/cost-benefit.md b/book/pd/risk-evaluation/cost-benefit.md
index 1ca12a46..317143b5 100644
--- a/book/pd/risk-evaluation/cost-benefit.md
+++ b/book/pd/risk-evaluation/cost-benefit.md
@@ -5,7 +5,7 @@ This section deals with simplified cost benefit analysis for risk reduction inte
 
 {numref}`costs_and_benefits` below shows an overview of effects of the Delta works that were built after the 1953 flood disaster in the Netherlands. The main aim of the delta works was to provide flood protection to the Southwest of the Netherlands. However, other effects included the agricultural benefits to the region (benefits) and the effects on environmental quality in the estuaries in which dams were built (costs or negative effects).
 
-```{list-table} Costs and benefits of the delta works
+:::{list-table} Costs and benefits of the delta works {cite:p}`don2003`
 :header-rows: 1
 :name: costs_and_benefits
 
@@ -25,7 +25,7 @@ This section deals with simplified cost benefit analysis for risk reduction inte
   - Economic stimulus for the water engineering sector
 * - 
   - National Pride
-```
+:::
 
 A choice has to be made for which effects in the CBA are evaluated in monetary terms. For some items, such as construction costs or economic risk reduction (see below) this is straightforward. For other items such as environmental effects or reduction of risk to life, approaches for monetary evaluation exist, but they are not standardized or undisputed.
 
@@ -33,9 +33,11 @@ A choice has to be made for which effects in the CBA are evaluated in monetary t
 
 When considering investments that primarily focus on risk reduction (e.g. dike reinforcement or reinforcing of buildings for earthquakes), the main benefits will consist of the reduction of expected economic damages. For a measure to be cost effective, the investments should be smaller than the risk reduction.
 
-$$
+```{math}
+:label: investment
+
 I < \Delta E(D)
-$$ (investment)
+```
 
 where:
 
@@ -46,9 +48,11 @@ This formula can also be used to calculated the benefit/cost ratio, i.e. $\Delta
 
 For investments that focus on prevention (i.e. reducing the failure probability of the system) {eq}`investment` can be formulated as follows. 
 
-$$
+```{math}
+:label: investment_prevention
+
   I < (P_{f,0} - P_{f, new}) D
-$$ 
+``` 
 
 where:
 
@@ -71,9 +75,11 @@ One can easily show that the investment would just be acceptable if the benefits
 
 Other types of interventions do not affect the probability of an accident, but focus on reducing the damages. They are often indicated as mitigation. An example in the field of flood management concerns raising buildings instead of reinforcing the dikes. In this case the criterion becomes:
 
-$$
+```{math}
+:label: investment_reduction
+
 I < P_{f,0}(D_0 - D_{new})
-$$
+``` 
 
 where: 
 
@@ -86,9 +92,11 @@ The foregoing assumes that both the costs and benefits are expressed in the same
 
 For such situations the failure probability is generally expressed per unit of time, mostly per year. That means that the risk (reduction) is expressed in terms of € per year, whereas the initial investments have the unit of €. The net present value of cost or benefit values over a future range of years can be calculated with {eq}`npv`. To calculate the net present value NPV [€] a discount rate  should be used. The discount rate represents a required return on an investment.
 
-$$
-  NPV = \sum_{t=1}^{T} \frac{C_t}{(1+r)^t}
-$$ (npv)
+```{math}
+:label: npv
+
+NPV = \sum_{t=1}^{T} \frac{C_t}{(1+r)^t}
+```
 
 where:
 
@@ -123,22 +131,28 @@ Normalized present value of a cost or benefit in year $t$ for different discount
 
 The previous paragraphs have focussed on the evaluation of economic costs and benefits of risk reduction interventions. Many of these interventions also directly focus on reducing injuries and fatalities. Examples are regulations and investments in traffic safety which have introduced measures such as airbags and seat belts. 
 
-In literature on risk management the economic valuation of human life is often depicted as a difficult problem as it raises numerous moral questions. Some claim it is unethical to put a price on human life because life is priceless. The actual expenditures on risk reducing prospects show however that the investment in the reduction of risks to humans is always finite. Different approaches are available for evaluating the costs of interventions in relation to the reduction of risk to human life, see ⚠️(Vrijling and van Gelder, (2000); Jongejan et al., (2005 ) for a further discussion of the various approaches. One of the options is to add the economic value  of  human fatalities to the economic damages, i.e. $D+N \cdot d$. The value of the number of lives lost can be determined with different approaches. 
+In literature on risk management the economic valuation of human life is often depicted as a difficult problem as it raises numerous moral questions. Some claim it is unethical to put a price on human life because life is priceless. The actual expenditures on risk reducing prospects show however that the investment in the reduction of risks to humans is always finite. Different approaches are available for evaluating the costs of interventions in relation to the reduction of risk to human life, see ⚠️{cite:t}`vrijling2000`; {cite:t}`jongejan2005` for a further discussion of the various approaches. One of the options is to add the economic value  of  human fatalities to the economic damages, i.e. $D+N \cdot d$. The value of the number of lives lost can be determined with different approaches. 
+
+Several approaches are based on so-called stated preferences.  A survey can reveal how much people are willing to pay, e.g. for safety measures. In these cases the value of statistical life (VoSL) is obtained from the willingness to pay expressed by respondents in surveys. For example, in the cost benefit analysis for the flood defences in the Netherlands a value of a statistical life of €6.7 million per fatality is used ⚠️{cite:p}`jeuken2013`. The Value of a Statistical life lost in traffic accidents is estimated at €2.6 million ⚠️ {cite:p}`SWOV2012`. 
 
-Several approaches are based on so-called stated preferences.  A survey can reveal how much people are willing to pay, e.g. for safety measures. In these cases the value of statistical life (VoSL) is obtained from the willingness to pay expressed by respondents in surveys. For example, in the cost benefit analysis for the flood defences in the Netherlands a value of a statistical life of €6.7 million per fatality is used ⚠️(Deltares, 2013). The Value of a Statistical life lost in traffic accidents is estimated at €2.6 million ⚠️(SWOV, 2012).
+<!--% MMMMM Previously cited as Deltares 2013, but paper wasn't found. 
+In the 2016 version of Lecture Notes there is a Deltares 2014 document but also this was not found.
+-->
 
 One alternative approach is based on so-called revealed preferences. The costs of saving and extra (statistical) life ($CSX$) for actual life-saving interventions that have been taken in the past can be determined. 
 
-$$
-    CSX = \frac{I}{\Delta E(N)}
-$$ 
+```{math}
+:label: csx
+
+CSX = \frac{I}{\Delta E(N)}
+```
 
 where:
 
 - $CSX$ the costs of saving an extra life [€/life year]
 - $\Delta E(N)$ the reduction of the expected number of fatalities per year
 
-An extensive study on  values in various sectors, see ⚠️Tengs et al. (1995) showed that these vary widely across sectors and even within sectors. The highest $CSX$ values are found for risks for small probability – large consequence events, for example in the nuclear domain. For such cases the expected number of fatalities is already small and investments in incremental safety are large.
+An extensive study on  values in various sectors, see ⚠️{cite:t}`tengs1995`, showed that these vary widely across sectors and even within sectors. The highest $CSX$ values are found for risks for small probability – large consequence events, for example in the nuclear domain. For such cases the expected number of fatalities is already small and investments in incremental safety are large.
 
 One other approach is to base the value of a human life on macro-economic indicators. Several metrics have been proposed that relate this value to a person’s economic production.
-Given the difficulties associated with economic valuation of human life and the associated risk reduction, it is decided in some domains to develop separate criteria for considering the risk to life. This topic is further elaborated in ⚠️section 3.5.
\ No newline at end of file
+Given the difficulties associated with economic valuation of human life and the associated risk reduction, it is decided in some domains to develop separate criteria for considering the risk to life. This topic is further elaborated in ⚠️ {ref}`safety_standards` Section.
\ No newline at end of file
diff --git a/book/pd/risk-evaluation/decision.md b/book/pd/risk-evaluation/decision.md
index 53715aa3..545851ce 100644
--- a/book/pd/risk-evaluation/decision.md
+++ b/book/pd/risk-evaluation/decision.md
@@ -1,7 +1,7 @@
 (decision)=
 # Decision Analysis
 
-Decision analysis, or decision-making under uncertain conditions is part of everyday life: when choosing to buy a lottery ticket or choosing to take an umbrella during cloudy weather. In contrast to the rather intuitive decision making in everyday matters, a structured analysis of different alternatives with associated risks, costs and benefits is very useful for decisions in (civil) engineering. This chapter offers a very basic introduction into the decision theory with applications to decision problems in the civil engineering domain. Further reference is made to the work by other scholars for more rigorous and detailed treatment of this topic. See, for example Raiffa and Schlaifer, (1961); Benjamin and Cornell, (1970).
+Decision analysis, or decision-making under uncertain conditions is part of everyday life: when choosing to buy a lottery ticket or choosing to take an umbrella during cloudy weather. In contrast to the rather intuitive decision making in everyday matters, a structured analysis of different alternatives with associated risks, costs and benefits is very useful for decisions in (civil) engineering. This chapter offers a very basic introduction into the decision theory with applications to decision problems in the civil engineering domain. Further reference is made to the work by other scholars for more rigorous and detailed treatment of this topic. See, for example {cite:t}`raiffa1961`; {cite:t}`benjamin1970`.
 
 ## Introduction
 
@@ -43,9 +43,11 @@ Although these decision rules are helpful in some cases, the probability of occu
 
 Therefore it is necessary to include information on the probabilities of circumstances and outcomes, in order to determine a rational action with the highest expected value of the benefit. This theory is known as the Bayesian decision theory. In a probabilistic or Bayesian decision framework the optimal action $a*$ is defined as the one maximizing the expected utility. The following formula is found for the case with discrete outcomes.
 
-$$
+```{math}
+:label: optimal_action 
+
 a^*: \max_a E(u(a, \theta))=\max _a \sum_\theta u(a, \theta_i) P(\theta_i)
-$$                                                                                                                                                  
+```                                                                                                                                                  
 In which $u(a, \theta)$ is the utility of action a under circumstance $\theta$ and $P(\theta)$ is the probability that circumstance $\theta_{i}$ occurs.
 
 A rational decision is choosing the action with the highest expected (utility) value or highest benefit if outcomes are expressed in monetary values. This is illustrated in the example below. Note that other examples in these lecture notes will also be based on monetary values.
@@ -109,7 +111,7 @@ In this case the expected outcome is larger for buying shares than for buying bo
 
 ````
 
-In the previous example, the number of circumstances is limited and the probability distribution of the circumstances is discrete. For many decision problems this is not the case. The state of nature, for instance, can assume many values that cannot be made discrete. This, for example, would have been the case if the dividend in example 0 had been a percentage of the profit. In such cases a probability density function can be used to characterize the spectrum of outcomes. Using a continuous form of formula (3.5), the expected value of various actions, and the optimal action / decision can be identified.
+In the previous example, the number of circumstances is limited and the probability distribution of the circumstances is discrete. For many decision problems this is not the case. The state of nature, for instance, can assume many values that cannot be made discrete. This, for example, would have been the case if the dividend in example 0 had been a percentage of the profit. In such cases a probability density function can be used to characterize the spectrum of outcomes. Using a continuous form of formula {eq}`optimal_action`, the expected value of various actions, and the optimal action / decision can be identified.
 
 In taking decisions with uncertainties, it appears that probabilistic calculation techniques are a valuable aid to reach a rational choice. This is particularly the case if risks are dependent on the possible decisions. In such cases, Bayesian decision theory minimizes the total costs (i.e. investment costs plus risk in terms of potential losses). This can best be illustrated by means of an example from the civil engineering domain.
 
@@ -154,7 +156,7 @@ $$
 height: 300px
 name: example-river-excavation-2
 ---
-Cross-section of excavation near a river. Hashed area indicates the clay layer, which is underlain by sand. The water pressure is given by $h$ and is directly related to the river level (Figure {numref}`example-river-excavation`). 
+Cross-section of excavation near a river. Hashed area indicates the clay layer, which is underlain by sand. The water pressure is given by $h$ and is directly related to the river level ({numref}`example-river-excavation`). 
 ```
 
 The effect of a drainage system in the construction site (see {numref}`example-river-excavation`) on the groundwater levels has been reviewed using groundwater flow calculations. It appears that it reduces the mean value of the maximum water levels to $\mu _{h}$ = 3.52m and the standard deviation remains the same. In this case the failure probability is reduced to 0.04. Such a drainage system costs €150,000.
diff --git a/book/pd/risk-evaluation/econ-optimization.md b/book/pd/risk-evaluation/econ-optimization.md
index 97a2c540..a165b753 100644
--- a/book/pd/risk-evaluation/econ-optimization.md
+++ b/book/pd/risk-evaluation/econ-optimization.md
@@ -3,18 +3,22 @@
 
 The previous sections have focussed on decisions for which the number of actions was limited, e.g. excavation with or without drainage and the associated costs and benefits. However, there are several situations in which the number of actions is unlimited. This occurs when the failure probability level has to be decided for a system that is yet to be designed, with an infinite number of design options. An example of this type of decision problem is the heightening of dikes, as in theory an unlimited amount of values can be chosen for the elevation, e.g. 2m, 5m, 5.1m, 5.11m, 5.1111m 6m etc.
 
-For this situation an economic optimization that takes into account the costs of increasing the safety level and reducing the risks can be applied to derive an optimal level of safety (or the optimal “failure probability”). The economic optimization was developed and applied by ⚠️van Dantzig (1956), to derive the optimal dike height for South Holland after the 1953 storm surge disaster, as will be further elaborated in the next section.
+For this situation an economic optimization that takes into account the costs of increasing the safety level and reducing the risks can be applied to derive an optimal level of safety (or the optimal “failure probability”). The economic optimization was developed and applied by ⚠️{cite:t}`vandantzig1956`, to derive the optimal dike height for South Holland after the 1953 storm surge disaster, as will be further elaborated in the next section.
 In the economic optimization the total costs ($C_{tot}$[€]) are determined, consisting of the investments $I$[€] in a safer system and the present value of the risk $R$[€].
 
-$$
-    C_{tot} = I + R
-$$
+```{math}
+:label: total_cost
+
+C_{tot} = I + R
+```
 
 The annual risk, or expected economic dagmage is found by:
 
-$$
-    E(D) = P_f D
-$$
+```{math}
+:label: expected_economic_damage
+
+E(D) = P_f D
+```
 
 where:
 
@@ -26,9 +30,12 @@ In this approach it is thus assumed that all damages are expressed in monetary t
 
 The present value of the risk for an infinite time horizon can be found as follows:
 
-$$
-    R = \frac{P_f D}{r}
-$$
+```{math}
+:label: present_value_risk
+
+R = \frac{P_f D}{r}
+```
+
 
 The risk can be reduced by constructing a safer system (a lower $P_f$), or limiting the damage (smaller $D$). In this case we assume prevention measures that focus on reducing the failure probability. The investments will become a function of the failure probability of the system, since increasing the safety will lead to an increase of costs. 
 
diff --git a/book/pd/risk-evaluation/safety-standards.md b/book/pd/risk-evaluation/safety-standards.md
index bf588756..2e4bacb1 100644
--- a/book/pd/risk-evaluation/safety-standards.md
+++ b/book/pd/risk-evaluation/safety-standards.md
@@ -5,7 +5,7 @@ When answering the question “how safe is safe enough” a merely economic trea
 
 Two aspects are typically considered when evaluating and regulating risks to the public: the total or population-wide effects, and the distribution of effects within the affected population. {numref}`risk_evaluation` summarizes these perspectives. The societal perspective is concerned with ‘total effect’ and the effects of large-scale accidents on the society, in terms of economic damages and life loss. The individual perspective is concerned with distributive justice (‘equity’), i.e. the distribution of harm over the population.
 
-As risk is often the by-product of an otherwise legitimate and advantageous activity, such as production or transportation, regulating risks is essentially a balancing act between economic and social activities on the hand and a sufficiently safe society on the other hand ⚠️(e.g. Jongejan, 2008): just as too lenient regulations are suboptimal, too stringent ones are too.
+As risk is often the by-product of an otherwise legitimate and advantageous activity, such as production or transportation, regulating risks is essentially a balancing act between economic and social activities on the hand and a sufficiently safe society on the other hand ⚠️(e.g. {cite:t}`jongejan2008`): just as too lenient regulations are suboptimal, too stringent ones are too.
 
 ```{list-table} Overview of perspectives on risk evaluation
 :header-rows: 1
@@ -24,7 +24,7 @@ As risk is often the by-product of an otherwise legitimate and advantageous acti
 
 ## Three Types of Risk
 
-Based on the general concepts described above, it has been proposed to evaluate risks based on three criteria ⚠️(TAW, 1985; Vrijling et al, 1995; 1998):
+Based on the general concepts described above, it has been proposed to evaluate risks based on three criteria ⚠️({cite:t}`TAW1985`, {cite:t}`vrijling1995`, {cite:t}`vrijling1998`):
 - limit the **invidual risk** to prevent that certain people are exposed to disproportionally large risks;
 - limit the **societal risk** to limit the risks of large scale accidents with many fatalities;
 - **Economic optimization** to balance investments in risk reduction from an economic point of view.
@@ -55,9 +55,12 @@ Two of these criteria have already been discussed elsewhere in this book. **Soci
 
 The individual risk due to an accident can be calculated with:
 
-$$
-  IR = P_f P_{d|f}
-$$
+```{math}
+:label: ind_risk
+
+IR = P_f P_{d|f}
+```
+
 
 where:
 - $IR$ the individual risk [1/year]
@@ -116,9 +119,11 @@ Schematic FN curve illustrating limit line formulation.
 
 The general formulation for such a limit line without horizontal of vertical cut-off equals:
 
-$$
-  1 - F_N(n) \leq \frac{C}{n^\alpha}
-$$ (limit_line)
+```{math}
+:label: limit_line
+
+1 - F_N(n) \leq \frac{C}{n^\alpha}
+```
 
 where:
 - $C$ a constant that determines the vertical position of the limit line
@@ -126,7 +131,7 @@ where:
   
 The limit line is called risk neutral[^neutral] if $\alpha=1$, since it places equal weight on exceedance probabilities and numbers of fatalities. If $\alpha = 2$, the limit is risk averse. This means that that the exceedance probability of 10 times as many fatalities should be 100 times lower. This has been motivated by public aversion to large numbers of fatalities. For example, the loss of 1000 people in one accident (e.g. a major explosion) could be valued differently than  1000 losses of 1 person in separate accidents (e.g. in traffic).
 
-For different applications limit lines have been developed with varying constants and steepness. Examples of application areas include industrial risks in the Netherlands (next section), dams in the United States and Canada, and chemical risks in Hongkong and the UK ⚠️(Jonkman et al., 2003).
+For different applications limit lines have been developed with varying constants and steepness. Examples of application areas include industrial risks in the Netherlands (next section), dams in the United States and Canada, and chemical risks in Hongkong and the UK ⚠️{cite:p}`jonkman2003`.
 
 
 ````{admonition} Exaple: Risk Matrix
@@ -143,7 +148,7 @@ Example of a risk matrix used by various engineering and military branches of th
 ## Case Study: Industrial Hazards
 
 The Dutch major hazards policy deals with the risks to those living in the vicinity of major industrial hazards such as chemical plants and LPG-fuelling stations. The development of the Dutch major hazards policy was strongly incident driven, as were European efforts aimed at the prevention of major industrial accidents. After a number of severe industrial accidents, including the Bhopal accident in 1984 which killed an estimated 3000 people and severely injured over 200.000, a European directive was drafted concerning the prevention of major accidents: the 1982 Seveso Directive. This was later replaced by the Seveso II Directive.
-The cornerstones of the Dutch major hazards policy are a) the use of quantitative risk analysis (QRA); b) comparison of QRA outcomes with limits to individual and societal risks ⚠️(Bottelberghs, 2000). 
+The cornerstones of the Dutch major hazards policy are a) the use of quantitative risk analysis (QRA); b) comparison of QRA outcomes with limits to individual and societal risks ⚠️{cite:p}`bottelberghs2000`. 
 
 Within the Dutch major hazards policy, individual risk is defined as the probability of death of an average, unprotected person that is constantly present at a certain location. It is thereby a property of location and iso-risk contours can be plotted on a map (see {numref}`risk_contour`). Individual risk is therefore also named local risk (“plaatsgebonden risico”) in the Netherlands. The shape of the risk contours for other applications will look different. For airports the contours will follow the shape of the runway and flight paths, for polders and flooding the risk contours will be highest in the deepest part of the polder. 
 
@@ -190,14 +195,14 @@ FN limit line used for installations in the Netherlands
 
 ## Case Study: Flood Protection
 
-For systems for which no regulations are available the question 'how safe is safe enough?' can be difficult to resolve. A general model has been developed by the Technical Advice Committee for Water defences (TAW)[^taw] in the process of deriving safety standards for flood protection in the Netherlands, which, in a formal sense, has been an ongoing process since the 1950's. This approach is derived on the basis of accident statistics ⚠️(TAW, 1985; Vrijling et al 1995; 1998), where underlying assumption of the model is that accident statistics are the result of a process of societal optimisation and that they thereby reflect what is apparently considered acceptable by society at large. Such an approach is commonly referred to as a 'revealed preference' approach, and was already introduced in a a general sense in the Chapter on Risk Analysis, Section {ref}`risk_curve_famous` ({numref}`risk-curve-baecher`).
+For systems for which no regulations are available the question 'how safe is safe enough?' can be difficult to resolve. A general model has been developed by the Technical Advice Committee for Water defences (TAW)[^taw] in the process of deriving safety standards for flood protection in the Netherlands, which, in a formal sense, has been an ongoing process since the 1950's. This approach is derived on the basis of accident statistics ⚠️({cite:t}`TAW1985`; {cite:t}`vrijling1995`, {cite:t}`vrijling1998`), where underlying assumption of the model is that accident statistics are the result of a process of societal optimisation and that they thereby reflect what is apparently considered acceptable by society at large. Such an approach is commonly referred to as a 'revealed preference' approach, and was already introduced in a a general sense in the Chapter on Risk Analysis, Section {ref}`risk_curve_famous` ({numref}`risk-curve-baecher`).
 
 ### Individual risk
 
 Accident statistics reveal that the extent to which participation in the activity is voluntary strongly influences the level of risk that is accepted by individuals. Relatively high individual risks are accepted for activities that are voluntary and have a (personal) benefit, such as mountain climbing. Much smaller individual risk values are accepted for involuntary activities for which the risks are imposed by others, e.g. for chemical and nuclear industry. A policy factor ($\beta$) is therefore introduced to account for voluntariness of exposure. This factor is set at $\beta=1$ for an individual risk value of $10^{-4}$ per year. This represents the “baseline” individual risk for the group young men[^men] who are most at risk of dying in a traffic accident.
 
 
-```{list-table} Accident statistics and proposed policy factor and characteristics of the activity ⚠️(Sources: CUR, 2015; Vrijling, 2001; Vrijling et al., 1998).
+:::{list-table} Accident statistics and proposed policy factor and characteristics of the activity ⚠️(Sources: {cite:t}`CUR2015`; {cite:t}`vrijling2001`; {cite:t}`vrijling1998`).
 :header-rows: 1
 :name: accident_statistics
 
@@ -231,7 +236,7 @@ Accident statistics reveal that the extent to which participation in the activit
   - 0.01
   - Involuntary
   - No benefit
-```
+:::
 
 The proposed individual risk limit becomes:
 
@@ -249,7 +254,7 @@ Note that the Dutch individual risk criterion for hazardous installations would
 
 ### Societal risk
 
-The societal risk criterion proposed by the TAW is based on the thought that societal risk should be evaluated primarily at a national level as local developments may lead to a situation that is considered unacceptable by society as a whole ⚠️(Vrijling et al., 1995). The societal risk criterion at a national scale proposed by the TAW is:
+The societal risk criterion proposed by the TAW is based on the thought that societal risk should be evaluated primarily at a national level as local developments may lead to a situation that is considered unacceptable by society as a whole ⚠️{cite:t}`vrijling1995`. The societal risk criterion at a national scale proposed by the TAW is:
 
 $$
   E(N) + k \sigma(N) \leq \beta \cdot 100
@@ -300,7 +305,7 @@ The resulting expected value and standard deviation are shown in {numref}`exp_va
   - 0.995
 ```
 
-The next step would be to distribute this maximum allowable level of societal risk over individual installations. After all, locally imposed societal risk criteria are necessary for achieving the desired national level of societal risk. The translation of the nationally acceptable level of risk to a criterion for a single local installation depends on the type of probability distribution of the number of fatalities. In ⚠️Vrijling et al (1998) a formulation of the risk acceptance at a local level is presented conform {eq}`limit_line`:️
+The next step would be to distribute this maximum allowable level of societal risk over individual installations. After all, locally imposed societal risk criteria are necessary for achieving the desired national level of societal risk. The translation of the nationally acceptable level of risk to a criterion for a single local installation depends on the type of probability distribution of the number of fatalities. In ⚠️{cite:t}`vrijling1998` a formulation of the risk acceptance at a local level is presented conform {eq}`limit_line`:️
 
 $$
   1 - F_n(n) \leq \frac{C}{n^\alpha}
@@ -317,7 +322,7 @@ in which $N_a$ is the number of independent locations where the activity takes p
 
 ### Combination of Risk Types
 
-According to the approach by TAW the three approaches (individual, societal and economic risk) lead to three acceptable failure probabilities. The most stringent of the three criteria can be chosen to determine the acceptable probability of failure of the system and to make sure that all three conditions are fulfilled. This can best be illustrated with an example (see below). The principles of this approach have been applied to derive the proposed new safety standards for flood defences in the Netherland by the ⚠️Delta Program (2014).
+According to the approach by TAW the three approaches (individual, societal and economic risk) lead to three acceptable failure probabilities. The most stringent of the three criteria can be chosen to determine the acceptable probability of failure of the system and to make sure that all three conditions are fulfilled. This can best be illustrated with an example (see below). The principles of this approach have been applied to derive the proposed new safety standards for flood defences in the Netherland by the ⚠️{cite:t}`delta2014`.
 
 ````{admonition} Example: Combination of individual, societal and economic risk for a dike ring area
 
-- 
GitLab


From 767a93aaf98765f0572da0b2d7a48a59b8a2a984 Mon Sep 17 00:00:00 2001
From: Antonio Magherini <A.Magherini@student.tudelft.nl>
Date: Mon, 14 Aug 2023 13:29:06 +0200
Subject: [PATCH 2/2] CUR 2015 removed from references

---
 book/_bibliography/references_pd.bib        | 7 -------
 book/pd/risk-evaluation/safety-standards.md | 4 +++-
 2 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/book/_bibliography/references_pd.bib b/book/_bibliography/references_pd.bib
index abed1c9d..6789c53c 100644
--- a/book/_bibliography/references_pd.bib
+++ b/book/_bibliography/references_pd.bib
@@ -336,13 +336,6 @@ title = {Cost-benefit Analysis of Flood Risk Management Strategies for the Rhine
    year = {2000},
 }
 
-@misc{CUR2015,
-  title={Kansen in de Civiele Techniek, Deel 1: Probabilistisch ontwerpen in theorie},
-  author={CUR, (Civieltechnisch Centrum Uitvoering research en Regelgeving)},
-  howpublished={CUR rapport 190},
-  year={2015}
-}
-
 @article{vrijling2001,
 title = {Probabilistic Design of Water Defense Systems in The Netherlands},
 journal = {Reliability Engineering & System Safety},
diff --git a/book/pd/risk-evaluation/safety-standards.md b/book/pd/risk-evaluation/safety-standards.md
index 2e4bacb1..95f5d639 100644
--- a/book/pd/risk-evaluation/safety-standards.md
+++ b/book/pd/risk-evaluation/safety-standards.md
@@ -202,7 +202,7 @@ For systems for which no regulations are available the question 'how safe is saf
 Accident statistics reveal that the extent to which participation in the activity is voluntary strongly influences the level of risk that is accepted by individuals. Relatively high individual risks are accepted for activities that are voluntary and have a (personal) benefit, such as mountain climbing. Much smaller individual risk values are accepted for involuntary activities for which the risks are imposed by others, e.g. for chemical and nuclear industry. A policy factor ($\beta$) is therefore introduced to account for voluntariness of exposure. This factor is set at $\beta=1$ for an individual risk value of $10^{-4}$ per year. This represents the “baseline” individual risk for the group young men[^men] who are most at risk of dying in a traffic accident.
 
 
-:::{list-table} Accident statistics and proposed policy factor and characteristics of the activity ⚠️(Sources: {cite:t}`CUR2015`; {cite:t}`vrijling2001`; {cite:t}`vrijling1998`).
+:::{list-table} Accident statistics and proposed policy factor and characteristics of the activity ⚠️(Sources: {cite:t}`vrijling2001`; {cite:t}`vrijling1998`).
 :header-rows: 1
 :name: accident_statistics
 
@@ -237,6 +237,8 @@ Accident statistics reveal that the extent to which participation in the activit
   - Involuntary
   - No benefit
 :::
+<!--% MMMMM The table cites also CUR 2015 but it wasn't found, maybe it's just an update of CUR 1997 (report 190)?
+-->
 
 The proposed individual risk limit becomes:
 
-- 
GitLab