Bahadori-Jahromi, Ali ORCID: https://orcid.org/0000-0003-0405-7146, Room, Shah, Paknahad, Chia, Altekreeti, Marwah, Tariq, Zeeshan and Tahayori, Hooman (2025) The role of Artificial Intelligence and Machine Learning in advancing civil engineering: a comprehensive review. Applied sciences, 15 (19).

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Abstract

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised
civil engineering, enhancing predictive accuracy, decision-making, and sustainability across
domains such as structural health monitoring, geotechnical analysis, transportation systems,
water management, and sustainable construction. This paper presents a detailed
review of peer-reviewed publications from the past decade, employing bibliometric mapping
and critical evaluation to analyse methodological advances, practical applications, and
limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering
domain, learning paradigm, and adoption maturity to guide future development.
Key applications include pavement condition assessment, slope stability prediction, traffic
flow forecasting, smart water management, and flood forecasting, leveraging techniques
such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support
Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The
review highlights challenges, including limited high-quality datasets, absence of AI provisions
in design codes, integration barriers with IoT-based infrastructure, and computational
complexity. While explainable AI tools like SHAP and LIME improve interpretability, their
practical feasibility in safety-critical contexts remains constrained. Ethical considerations,
including bias in training datasets and regulatory compliance, are also addressed. Promising
directions include federated learning for data privacy, transfer learning for data-scarce
regions, digital twins, and adherence to FAIR data principles. This study underscores AI as
a complementary tool, not a replacement, for traditional methods, fostering a data-driven,
resilient, and sustainable built environment through interdisciplinary collaboration and
transparent, explainable systems.

Item Type:	Article
Identifier:	10.3390/app151910499
Keywords:	artificial intelligence; civil engineering; machine learning; predictive modelling; sustainability; infrastructure management
Subjects:	Construction and engineering > Civil and environmental engineering Computing
Date Deposited:	29 Sep 2025
URI:	https://repository.uwl.ac.uk/id/eprint/14123
Sustainable Development Goals:	Goal 7: Affordable and Clean Energy	Sustainable Development Goals:	Goal 9: Industry, Innovation, and Infrastructure	Sustainable Development Goals:	Goal 11: Sustainable Cities and Communities	Sustainable Development Goals:	Goal 13: Climate Action

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