Reliability Engineering and System Safety

The provided data includes statistics for the flood depth and flood duration resistance for each component in terms of mean and standard deviation of this resistance along with their upper and lower bounds. Additionally, these components were assigned to prescribed damage states based on the description provided in the paper in Table (2). Component descriptions are also provided to indicate how each dataset was developed. Some assumptions related to the flood depth and duration resistance are from van de Lindt and Taggart [1,2] and others are from the experimental investigation by Aglan [3]. Additionally, some engineering assumptions related to flood depth and duration were made by the authors based on engineering judgment to be able to create a full probabilistic damage model; these are explicitly stated in the manuscript. The mean unit price of each component was also provided based on data collected from different online sources that use an extensive cost database from contractors biding all over the US such as Home Advisor [4], Home Guides [5], and UpCodes [6]. These source gives the prices in terms of the upper and lower bounds based on the building location and other parameters related to labor cost, materials used, etc. Therefore, minimum, maximum and mean value of the replacement cost of each component was provided along with their standard deviations calculated using the Range Rule of Thumb [7]. Furthermore, the mean and standard deviation of each damage state replacement cost are provided in USD and also provided as a percentage of the total building replacement cost. References [1] van de Lindt JW, Taggart M. Fragility Analysis Methodology for Performance-Based Analysis of Wood-Frame Buildings for Flood. Nat Hazards Rev 2009;10:113–23. doi:10.1061/(ASCE)1527-6988(2009)10:3(113). [2] Taggart M. THESIS Performance based design of wood frame structures for flooding. 2007. [3] Aglan H. Field testing of energy-efficient flood-damage-resistant residential envelope systems summary report. Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States); 2005. [4] Home Advisor 2019. https://www.homeadvisor.com/?gclid=EAIaIQobChMI9OOambL05AIVExx9Ch3E3w6zEAAYASAAEgIrb_D_BwE. [5] Home Guides 2019. https://homeguides.sfgate.com/. [6] Up Codes 2019. https://up.codes/. [7] Triola MF. Elementary statistics. Pearson/Addison-Wesley Reading, MA; 2010.

The zip file contains R scripts needed to reproduce the results in Sections 4.1 and 4.2 of the manuscript.

This Appendix describes the details of the ABM developed in the NetLogo® software environment for simulation purposes.

This dataset contains supporting data for the paper titled: A Novel approach to Risk-Informed Decision-Making under non-ideal Instrumentation and Control conditions through the Application of Bayes' Theorem.

The functions used to fit the models are bundled in an R package called tailvalidation. The user should first install this R package in R (using either the command install.packages or the drop-down menu Tools -> Install packages). Then, the examples from the paper can be reproduced using the RESS_example.R file.

Egress route data for 16 participants in 11 simulated scenarios are associated with this article. A total of 7 attributes were varied across scenarios. Different attributes and their possible values are listed in Table 1 in [1]. Details of the attribute value assignment are discussed in Section 3.2 in [1]. Depending on the values of the attributes, participants took either the primary or the secondary route to egress. Participants’ route selection in each scenario was recorded.

These data include aviation accidents investigated by the National Transportation and Safety Board. The data have been codified to indicate reported causal factors using the Human Factors Analysis and Classification System. Included in these is the accident severity, flight segment and operating category. These data are binary coded data to enable conducting multiple variable logistic regression. Totals and pertinent percentages are included. The data were analyzed using Minitab 17 and the results are reported in the paper.

The data and code files presented here are developed as part of Yue Shang's PhD Thesis project and are associated with the following publication. They are being made publicly available as supplementary data for the thesis of Yue Shang for review and reproduction purposes. Shang, Y., Nogal, M., Teixeira, R., & Wolfert, A. R. M. (2024). Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems. Reliability Engineering & System Safety, 243, 109818. Furthermore, the methodology proposed in this work extends upon previous research (as shown below) by integrating limit state design considerations into the formulation of extreme-based engineering problems. Therefore, the code for this project is built upon the foundation of the following work. Nogal, M., & Nogal, A. (2021). Sensitivity method for extreme-based engineering problems. Reliability Engineering & System Safety, 216, 107997. For case studies used in this project, a finite element model to simulate train-track-bridge dynamic interaction was employed, which is licensed under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html). The details are referred to the following publication. Cantero, D. (2022). TTB-2D: Train–Track–Bridge interaction simulation tool for Matlab. SoftwareX, 20, 101253. Further details can be found in the attached README file.

The data and code files presented here are developed as part of Yue Shang's PhD Thesis project and are associated with the following publication. They are being made publicly available as supplementary data for the thesis of Yue Shang for review and reproduction purposes. Shang, Y., Nogal, M., Teixeira, R., & Wolfert, A. R. M. (2024). Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems. Reliability Engineering & System Safety, 243, 109818. Furthermore, the methodology proposed in this work extends upon previous research (as shown below) by integrating limit state design considerations into the formulation of extreme-based engineering problems. Therefore, the code for this project is built upon the foundation of the following work. Nogal, M., & Nogal, A. (2021). Sensitivity method for extreme-based engineering problems. Reliability Engineering & System Safety, 216, 107997. For case studies used in this project, a finite element model to simulate train-track-bridge dynamic interaction was employed, which is licensed under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html). The details are referred to the following publication. Cantero, D. (2022). TTB-2D: Train–Track–Bridge interaction simulation tool for Matlab. SoftwareX, 20, 101253. Further details can be found in the attached README file.

This software supports: Bodenmann L., Reuland Y. and Stojadinovic B. (2023): Dynamic Post-Earthquake Updating of Regional Damage Estimates Using Gaussian Processes; Reliability Engineering and System Safety. doi: 10.1016/j.ress.2023.109201 See the readme file on GitHub for further information.