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Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B.
Paper No: RISK-24-1060
Published Online: October 30, 2024
Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B.
Paper No: RISK-24-1059
Published Online: October 24, 2024
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011201.
Paper No: RISK-24-1062
Published Online: October 23, 2024
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 1 Example of corridors computed by the solution space method for crashworthiness. d 1 and d 2 are the expected deformation lengths for each component, while F 1 , 2 l and F 1 , 2 u are the lower and upper bounds of the comp... More about this image found in Example of corridors computed by the solution space method for crashworthin...
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 2 Flowchart with the overview of the entire process for training the crashworthiness classifier More about this image found in Flowchart with the overview of the entire process for training the crashwor...
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 3 From the 2013 Honda Accord model [ 2 ] to the solution space corridors [ 23 ], via Deformation Space Model [ 18 ] of the car considering seven components. In the solution space corridors, the white area represents the feasible force ranges, while the gray area represents the infeasible forc... More about this image found in From the 2013 Honda Accord model [ 2 ] to the solution space corridors [ 23...
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 4 Setup of the droptower test of the studied crashbox More about this image found in Setup of the droptower test of the studied crashbox
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 5 Corridors of the crashbox [ 23 ] More about this image found in Corridors of the crashbox [ 23 ]
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 6 Convergence of the percentage of misclassified samples: ( a ) the percentage of the 10,000 samples misclassified by the trained classifiers; ( b ) the percentage of the samples classified as feasible that have been misclassified in magenta, and the percentage of the samples classified as in... More about this image found in Convergence of the percentage of misclassified samples: ( a ) the percentag...
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 7 Confusion matrix of the trained classifier with the median performance More about this image found in Confusion matrix of the trained classifier with the median performance
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 8 Comparison between the feasible areas estimated by the trained classifier and defined by the validation set of 10,000 simulations. The gray area is the feasible area according to the validation set, the magenta area is the estimated feasible area. The 12 slices are taken for the following v... More about this image found in Comparison between the feasible areas estimated by the trained classifier a...
Image
in Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 23, 2024
Fig. 9 Evolution of the estimated feasible area during training. Each row shows three slices placed at t 1 = 1.0 mm, t 2 = 1.0 mm, and m = 30 kg. The first row shows the estimated feasible area after the evaluation of 50 samples (20 points of initial DoE an... More about this image found in Evolution of the estimated feasible area during training. Each row shows th...
Journal Articles
Multi-State Reliability Modeling and Assessment for Corrosion of Organic Coating-Substrate Structure
Accepted Manuscript
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B.
Paper No: RISK-24-1016
Published Online: October 19, 2024
Journal Articles
Accepted Manuscript
Zheng Liu, Yanwen Xu, Yuan Jiang, Anabel Renteria, Parth Bansal, Chenlong Xu, Pingfeng Wang, Yumeng Li
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B.
Paper No: RISK-24-1031
Published Online: October 19, 2024
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. June 2025, 11(2): 021204.
Paper No: RISK-22-1043
Published Online: October 14, 2024
Image
in Data-Informed Risk Analysis of Power Grids: Application of Method for Managing Heterogeneous Datasets
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 14, 2024
Fig. 1 Schematic representation of the two main vegetation-related power grid outage scenarios More about this image found in Schematic representation of the two main vegetation-related power grid outa...
Image
in Data-Informed Risk Analysis of Power Grids: Application of Method for Managing Heterogeneous Datasets
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 14, 2024
Fig. 2 Risk assessment framework augmentation proposed in Ref. [ 25 ]. The steps highlighted (1 1 , 1 2 , 1 5 , 2, 3 1 , 4 1 ) are the subject of augmentation (reinforcement actions 1 and 2). Those enable to (1)define the optimal level of analysis and (2) adequately characterize the problem. Preci... More about this image found in Risk assessment framework augmentation proposed in Ref. [ 25 ]. The steps h...
Image
in Data-Informed Risk Analysis of Power Grids: Application of Method for Managing Heterogeneous Datasets
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 14, 2024
Fig. 3 Simplified representation of the Three-Phases method logic [ 25 ]. A technological quality assessment enables to define the DMPK of a data source (i.e., how much of the required information can be provided by the data source for a specific parameter). Applying the two degradation factors (D... More about this image found in Simplified representation of the Three-Phases method logic [ 25 ]. A techno...
Image
in Data-Informed Risk Analysis of Power Grids: Application of Method for Managing Heterogeneous Datasets
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 14, 2024
Fig. 4 Examples of analysis level options with respective detailing possibilities for an item of interest selected for a risk analysis. The levels of analysis 1, 2, and 3 represent three ways of isolating that item, for instance, on national, regional, and municipal level, respectively. More about this image found in Examples of analysis level options with respective detailing possibilities ...
Image
in Data-Informed Risk Analysis of Power Grids: Application of Method for Managing Heterogeneous Datasets
> ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Published Online: October 14, 2024
Fig. 5 Further decomposition possibilities of the item of interest isolated on the third level of analysis. This fine-graining gives the possibility to isolate up to 69 items in the sixth level, which can then individually be considered for a risk analysis. More about this image found in Further decomposition possibilities of the item of interest isolated on the...
1
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Review of Uncertainty, Modeling, and Decision Making in Geotechnics , edited by Kok-Kwang Phoon, Takayuki Shuku, and Jianye Ching
Anand Govindasamy, Ph.D.
Article Type: Book Reviews
Published: July 15, 2024
Risk Comparison of Hurricane Scenarios as Disruptions of Hydrologic Basin Order with Social Vulnerability Criteria
Gigi Pavur; James H. Lambert; Venkataraman Lakshmi
Article Type: Research-Article
Published: July 15, 2024
Risk Identification, Prioritization, and Mapping for Early Construction Cost Contingency in Transportation Infrastructure Projects
Taewoo Ko; Chau Le; H. David Jeong; Phuong Nguyen; Kunhee Choi; Douglas Gransberg
Article Type: Research-Article
Published: July 12, 2024
Modeling of Chloride Spatial Variability in a Reinforced Concrete Wharf from Onsite Measurements
Romain Clerc; Franck Schoefs; Mestapha Oumouni; Inès Othmen; Stéphanie Bonnet
Article Type: Research-Article
Published: July 05, 2024
Proposed Bayesian Network Framework to Model Multisite Seismic Hazard with Existing Probabilistic Seismic Hazard Analysis Results
Emily M. Gibson, Ph.D.; Michelle T. Bensi, Ph.D.
Article Type: Research-Article
Published: July 05, 2024
A Simulation Model for Container Ships Scheduling in Qinzhou Port under Seamless Connection Mode
Wuliu Tian; Jinfen Zhang; Da Wu, Ph.D.; Yan Zhou; Yougui Yang
Article Type: Research-Article
Published: June 17, 2024
Data-Driven Telecommunication Outage Prediction during Hurricane Events
Ao Du
Article Type: Research-Article
Published: June 12, 2024
An Integrated Framework for Analyzing Risk Influence Factors of Inland Waterway Transport Based on Interpretive Structural Models and Bayesian Networks
Tao Guo; Lei Xie; Jinfen Zhang; Jianwei Zhao; Heyu Zhou, Ph.D.
Article Type: Research-Article
Published: June 11, 2024
Track Geometry Estimation and Hanging Sleeper Detection Using Vehicle Dynamic Responses with Unknown System Parameters
Masaru Kitahara; Daichi Masuda; Kai Xue, Ph.D.; Tomonori Nagayama, Ph.D.; Di Su, Ph.D.; Hirofumi Tanaka, Dr.Eng.
Article Type: Research-Article
Published: June 05, 2024
Optimum Seismic Retrofit of Deteriorating RC Bridge Columns Based on Risk, Cost–Benefit, and Real Option Analyses
Sunyong Kim; Yanzhi Yang; Dan M. Frangopol
Article Type: Research-Article
Published: June 04, 2024
New Approach for Conditional Coring in RC Structures Using Bivariate Distributions of Nondestructive Test Results
Soubhagya Karmakar; Siddhartha Ghosh; Saha Dauji
Article Type: Research-Article
Published: May 30, 2024
Investigation of Intraclass Correlation of Seismic Capacity for RC Bridge Piers Based on Hierarchical Model
Libo Chen; Ruchun Mo; Yu Chen; Zhan Guo; Zhenfeng Zheng
Article Type: Research-Article
Published: May 24, 2024
Comprehensive Risk System Analysis and Factor Coupling in Underground Railway Space
Xiaojuan Li; Lulu Li; Rixin Chen; C. Y. Jim
Article Type: Research-Article
Published: May 21, 2024
Design Optimization with Variable Screening by Interval-Based Sensitivity Analysis
Qi Chang; Changcong Zhou; Matthias G. R. Faes; Marcos A. Valdebenito
Article Type: Research-Article
Published: May 21, 2024
Evaluating Epistemic Uncertainty from Random Variables at the Terminal Branches of a Logic Tree
Scott J. Brandenberg, Ph.D.; Renmin Pretell, Ph.D.
Article Type: Technical Notes
Published: May 16, 2024
A Decision Support Methodology for Seismic Design Requirements of Buildings to Achieve Community-Level Resilience Metrics
Omar A. Sediek, Ph.D.; Milad Roohi, Ph.D.; John W. van de Lindt, Ph.D.
Article Type: Research-Article
Published: May 14, 2024
An Approach to Quantifying the Distribution of Resonance and Cancellation Train Speeds of Railway Bridges with Random Uncertainties
Lifeng Xin; Menglin Pei; Di Mu; Dangxiong Wang; Chao Li; Zhiqiang Wan
Article Type: Research-Article
Published: May 06, 2024
Collapse Failure Assessment of Geomaterials behind Steel Structure in Tunnels Using the Chebyshev Inequalities
Xiao-Cheng Huang; Gui-Lin Wang; Qiu-Nan Chen; Wei Zhang
Article Type: Technical Notes
Published: May 03, 2024
An Optimized Site Selection Method for Human-Life Search and Rescue in Inland Waters
Fan Zhang; Xin Peng; Yefeng Sun; Qing Yu; Yuanqiao Wen
Article Type: Research-Article
Published: May 02, 2024
Integrating Bluetooth-Enabled Sensors with Cloud Computing for Fire Hazard Communication Systems
Benben Cui; Chen Wang; Meng Wu; Can Zhu; Defa Wang; Bin Li
Article Type: Research-Article
Published: April 30, 2024