Artificial Intelligence for Infrastructure Resilience: Transportation Systems as a Strategic Case for Policy and Practice

Olusola O. Ajayi (), Anish Kurien, Karim Djouani and Lamine Dieng
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Olusola O. Ajayi: F’SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa
Anish Kurien: F’SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa
Karim Djouani: F’SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa
Lamine Dieng: F’SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa

Sustainability, 2025, vol. 17, issue 20, 1-18

Abstract: Transportation networks are critical lifelines in national infrastructure but are increasingly exposed to risks arising from climate variability, cyber threats, aging assets, and limited resources. This paper presents a scoping review of 58 peer-reviewed studies published between 2015 and 2025 that examine the role of Artificial Intelligence (AI) in strengthening infrastructure resilience, with transportation systems adopted as the strategic case. The review classifies applications along five dimensions: technological approach, infrastructure sector, transportation linkage, resilience/security aspect, and key research gaps. Findings show that AI, machine learning (ML), and the Internet of Things (IoT) dominate current applications, particularly in predictive maintenance, intelligent monitoring, early-warning systems, and optimization. These applications extend beyond transport to energy, water, and agri-food systems that indirectly sustain transport resilience. Persistent challenges include affordability, data scarcity, infrastructural limitations, and limited real-world validation, especially in Sub-Saharan African contexts. The paper synthesizes cross-sector pathways through which AI enhances transport resilience and outlines practical implications for policymakers and practitioners. A targeted research agenda is also proposed to address methodological gaps, enhance deployment in resource-constrained settings, and promote hybrid and explainable AI for trust and scalability.

Keywords: artificial intelligence; infrastructure resilience; transportation systems; scoping review; machine learning; IoT; predictive maintenance; early-warning; policy and practice; sub-Saharan Africa (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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