AI-Powered Risk Assessment and Failure Prediction in Smart Structural Systems

DOI

10.2991/978-94-6239-695-1_9How to use a DOI?

Keywords

Artificial Intelligence; Structural Health Monitoring; Smart Infrastructure; Risk Assessment; Failure Prediction; Deep Learning; Structural Integrity; Machine Learning; Civil Engineering; Predictive Maintenance

Abstract

The structural integrity of civil infrastructure is paramount to ensuring safety, resilience, and sustainability in a rapidly urbanizing world. Traditional methods of structural health monitoring (SHM) often rely on deterministic and empirical models, which struggle to capture the complexity and stochastic nature of structural behavior under dynamic conditions. With the advent of smart structures embedded with sensors and real-time data acquisition systems, artificial intelligence (AI) offers an unprecedented opportunity to revolutionize risk assessment and failure prediction. This study presents a comprehensive AI-driven framework for structural risk analytics, integrating sensor-based data streams with advanced machine learning and deep learning models to detect anomalies, assess failure probability, and forecast structural degradation. A hybrid predictive architecture combining convolutional neural networks (CNNs), long short-term memory (LSTM) units, and ensemble learning techniques was developed and validated on real-world datasets from bridge and high-rise monitoring systems. The findings reveal that AI models not only outperform traditional rule-based diagnostics in accuracy and lead time but also adapt dynamically to nonlinear material behavior, environmental stressors, and cumulative fatigue. By embedding predictive intelligence into smart infrastructures, this research paves the way for proactive maintenance, resource optimization, and life-cycle resilience in critical structural systems. The implications are transformative, promising safer urban environments and informed decision-making in structural engineering.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Sukhdeep Kaur
AU  - Shalom Akhai
AU  - Sana Abass Wani
AU  - Mahapara Abbass
AU  - Amandeep Singh Wadhwa
AU  - Abhishek Chauhan
PY  - 2026
DA  - 2026/05/30
TI  - AI-Powered Risk Assessment and Failure Prediction in Smart Structural Systems
BT  - 2nd International Conference on Advanced Materials & Devices for Futuristic Applications-2024 (IC-AMDFA 2024)
PB  - Atlantis Press
SP  - 145
EP  - 162
SN  - 2590-3217
UR  - https://doi.org/10.2991/978-94-6239-695-1_9
DO  - 10.2991/978-94-6239-695-1_9
ID  - Kaur2026
ER  -