PSI - Issue 64

Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2023) 000–000 Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 64 (2024) 21–28

SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Empowering Engineers by Leveraging AI in Structural Engineering and Monitoring Shirley J. Dyke a,b *, Xiaoyu Liu a , Xin Zhang b , Lissette Iturburu b

a School of Mechcanical Engineering, Purdue University, West Lafayette, IN, 47906, US b Lyles School of Civil Engineering,, Purdue University, West Lafayette, IN, 47906, US

Abstract Structural engineers play a critical role in providing safe, durable, and sustainable infrastructure to connect our communities and drive our economy. The recent explosion in opportunities to integrate artificial intelligence (AI) into the daily activities of structural engineers offers potential to reduce the workload on the human engineer, giving them more time to focus on creative tasks that require technical expertise and experience. Leveraging AI to support the human engineer has been the focus of several of recent innovative studies conducted by the Intelligent Infrastructure Systems Lab (IISL) at Purdue University. This work has addressed several fundamental challenges related to inefficiencies in the collection, interpretation, analysis, and automated use of large volumes of data, breaking several past barriers to the use of AI methods toward the practice of civil engineering. This paper explores how these innovations are enabling human engineers to harness AI to automate tasks related to extracting information and content from images, providing context to images, and fusing data to support decision- or policy-making. The implications of this integration extend beyond operational efficiencies, offering a pathway to smarter and more resilient infrastructure systems. © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers Keywords: Computer vision; artificial intelligence; structural health monitoring © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers

* Corresponding author. Tel.: 1-765-494-7434; fax:+0-000-000-0000 . E-mail address: sdyke@purdue.edu

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 10.1016/j.prostr.2024.09.203