An approach for data-driven time-varying flood resilience quantification of housing infrastructure system

ABSTRACT

Resilience is the ability of infrastructure systems possessing a sufficient acclimatizing property to absorb hazards and return to normalcy post-damage in the least possible amount of time. Quantification of infrastructure resilience requires data collection, which must be accomplished cautiously using the available resources and securing key collaborations with local agencies to fasten and ease the mammoth process. Herein, the collected data were fed into the developed evidence-based Dempster-Shafer (D-S) model to quantify resilience over the desired frame of targeted output possibilities. The data collection procedure was performed with proper predetermined objectives and tools. The subject matter of this research, viz. flood resilience is a time-dependent phenomenon, with a typically higher recovery, as flooding is a long seasonal occurrence. To this end, data has been collected during seven different periods spanning 7 months of the rainy season, the resilience indices were calculated, and profiling was conducted to understand the resilience behavior. In this work, Barak Valley test bed in the North-East India is considered. The resilience indices are utilized as a performance indicator of housing infrastructure resilience, which help to support the decisions of the critical infrastructure owners/operators in risk and resilience management.

KEYWORDS:

• Resilience index
• time-varying resilience
• flood
• housing infrastructure
• Dempster-Shafer (D-S) theory

View correction statement:

Correction

Acknowledgments

The third author acknowledges the support extended by District Disaster Management Authority, Cachar, Assam, for providing the technical/administrative support during this work. The first author acknowledges the students’ scholarship received from the Ministry of Education, Government of India.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The sample data collection sheet and the data used for analysis, resilience assessment form, resilience index values for time period pairs from t₀ to t₆ and the associated computer programs can be made available on request from the corresponding author.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23789689.2023.2246336

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (https://doi.org/10.1080/23789689.2023.2246336)

Additional information

Funding

This work was supported by the Science and Engineering Research Board [AV/VRI/2022/0246].

Notes on contributors

Jahir Iqbal Laskar

Jahir Iqbal Laskar is currently working as a Research Scholar and Teaching Assistant in the Department of Civil Engineering, National Institute of Technology Silchar, India. He is a graduate of Civil Engineering from Jorhat Engineering College, Assam, India and, has done Masters in Structural Engineering from National Institute of Technology Silchar. Presently, he is involved in works that focus on providing practical solutions to real-life engineering problems for sustainable development. His research interest includes the application of probability and statistics in civil infrastructure engineering.

Mrinal Kanti Sen

Mrinal Kanti Sen is currently working as an Assistant Professor in Civil Engineering at Assam Don Bosco University, Guwahati, India. He completed B.Tech (Civil Engineering) from NIT Agartala, M.Tech (Structural Engineering) from Lovely Professional University and,Ph.D. (Structural Engineering) from National Institute of Technology Silchar, India. He was granted an amount 5000 Canadian $ Award as - QUEEN ELIZABETH II DIAMOND JUBILEE (QES) scholar in 2019 by University of Regina, Canada for his outstanding research achievements. His research interests include Structural health monitoring, Reliability, MCDM tools, Resilience, Uncertainty quantification, to mention a few.

Subhrajit Dutta

Subhrajit Dutta is an Assistant Professor in the Department of Civil Engineering at National Institute of Technology Silchar. In addition to his service at NIT Silchar, he is also an external/invited researcher in the Department of Civil Engineering (Structural Division), Faculty of Engineering, University of Porto, Portugal. He was working as a visiting professor in the Department of Civil Engineering at Aalto University, Finland. Subhrajit’s research group is currently working on areas like, probabilistic structural mechanics, system reliability/risk analysis, uncertainty quantification, structural optimization under uncertainty, infrastructure & community resilience, application of AI/ML in structural engineering. Subhrajit completed his Ph.D. in Civil Engineering from Indian Institute of Technology Bombay in 2017 with a Government of India scholarship. Recently, he received the prestigeous CDRI fellowship and also have been awarded with the first position for IEEE Connecting The Unconnected (CTU) challenge. He has received prestigious National/International research grants and has published one textbook and more than 100 research articles till date. Subhrajit is the Associate Editor of ASCE Practice Periodical on Structural Design and Construction, and also working in the editorial board of many reputed journals and book series in the areas of structure and infrastructure engineering.

Amir H Gandomi

Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. He is also affiliated with Obuda University, Budapest, as a Distinguished Professor. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at Stevens Institute of Technology, USA and a distinguished research fellow at BEACON center, Michigan State University, USA. Prof. Gandomi has published over three hundred journal papers and 12 books which collectively have been cited 43,000+ times (H-index = 93). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1% researchers) from Web of Science for six consecutive years, from 2017 to 2022. In the recent most impactful researcher list, done by Stanford University and released by Elsevier, Prof Amir H Gandomi is ranked among the top 1,000 researchers (top 0.01%) and top 50 researchers in AI and Image Processing subfield in 2021! He also ranked 17th in GP bibliography among more than 15,000 researchers. He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals, such as AE of IEEE Networks and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.

Sujit Tewari

Sujit Tewari is an Assistant Professor in the Department of Physics, Karimganj College, Karimganj, Assam, India . He obtained M.Sc. and M.Phil. in Physics from Assam University and Ph.D. in Physics from National Institute of Technology Silchar. He has been a senior research fellow of the Council of Scientific and Industrial Research (CSIR), Government of India. He has been an active researcher and has published a good number of research papers in reputed research Journals. His area of research has mainly focused on material properties and their applications at nano scale.