Investigating the Role of Artificial Intelligence in Enhancing Decision-Making and Structural Health Monitoring Processes in Construction Engineering and Management: A Literature Review

Abstract

Artificial Intelligence (AI) is gaining an increasing attention in the Construction Engineering and Management (CEM) industry for its potential to support decision-making processes, improve operational efficiency, and enhance overall infrastructure safety. However, existing research remains fragmented with limited integration between studies focusing on AI- enabled decision-making and those addressing Structural Health Monitoring (SHM).

This study conducts a systematic review to examine how AI contributes to these critical areas in the industry. The review followed an approach to ensure transparency and academic rigor by conducting a comprehensive search through different databases. A total of 89 articles were selected based on defined inclusion and exclusion criteria.

Findings reveal a growing application of AI in predictive analytics, risk mitigation, real-time analysis and proactive maintenance. However, challenges such as organizational resistance, workforce readiness, data related concerns, and regulatory constrains remain not fully explored. The study proposes a conceptual framework that integrates two separate research area into one unified framework, AI-driven decision making and SHM, linking safety and efficiency outcomes together. The framework offers a structured foundation for future empirical testing and addresses critical gaps in current theory by explaining both the mechanisms and contextual factors shaping AI’s effectiveness in real-world construction settings.

The study contributes to theory by offering a systems-oriented perspective that connects technological, human, and organizational factors. Practically, it provides a foundation for developing guidelines and future empirical studies aimed at validating the framework.