Revolutionizing Structural Engineering: Applications of Machine Learning for Enhanced Performance and Safety

Abstract

This study delves into the transformative influence of Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) within the realm of Structural Engineering, emphasizing their profound implications for Information, Process, and Design Engineering. Through a meticulous analysis of existing literature, the study highlights the vast potential of ML, DL, and AI across diverse construction domains, particularly within structural engineering, including healthcare, performance evaluation, monitoring, and optimization. Notably, the integration of ML with the Internet of Things (IoT) for real-time structural health monitoring emerges as a pivotal advancement, promising enhanced durability and performance models. Moreover, the application of ML-supported multi-objective optimization in design processes showcases promising strides, effectively balancing factors such as cost and durability to bolster structural integrity. By leveraging these technologies to process data, identify patterns, and predict behaviour, structural health is significantly bolstered. Moving forward, the study advocates for continued exploration of ML and IoT integration for real-time monitoring, refinement of learning algorithms for process control, and the utilization of ML-assisted multi-objective optimization in design. Crucially, it underscores the imperative of addressing challenges such as data availability and algorithm robustness to fully harness the potential of ML, DL, and AI in revolutionizing structural engineering design. This research thus serves as a clarion call for further investigation and training to facilitate the widespread adoption of these transformative technologies in structural engineering practices.