Investigating the Role of Artificial Intelligence in Enhancing Decision-Making and Structural Health Monitoring Processes in Construction Engineering and Management: A Literature Review
Loading...
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
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.
Description
This thesis explores how Artificial Intelligence (AI) is transforming the Construction Engineering and Management (CEM) field by strengthening decision-making processes and advancing Structural Health Monitoring (SHM). Through a systematic literature review encompassing 89 peer-reviewed studies, the research investigates the extent to which AI-driven tools and models are improving predictive capabilities, operational efficiency, and safety management within construction projects.
The analysis reveals 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.
To bridge the divide between decision-making and SHM research, the study introduces an integrative conceptual framework that links two separate research area into one unified framework, AI-driven decision making and SHM, linking safety and efficiency outcomes together. This framework offers a unified perspective that accounts 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.
By providing a structured foundation for future empirical studies, this work contributes to both academic theory and professional practice. It emphasizes the need for cross-disciplinary collaboration to ensure that AI adoption in CEM leads to more resilient, efficient, and data-informed infrastructure systems.