Development of Intelligent Technologies in SHM on the Innovative Diagnosis in Civil Engineering—A Comprehensive Review

Abstract

This comprehensive review focuses on the integration of intelligent technologies, such as the Internet of Things (IoT), Artificial intelligence (AI), and Nondestructive Testing (NDT), in the Structural Health Monitoring (SHM) field of civil engineering. The article discusses intelligent technologies in SHM for residential, commercial, industrial, historical, and special buildings, such as nuclear power plants (NPPs). With the incorporation of intelligent technologies, there have been remarkable advancements in SHM, a crucial aspect of infrastructure safety, reliability, and durability. The combination of SHM and intelligent technologies provides a cost-effective and efficient building monitoring approach, significantly contributing to energy and resource conservation. This article explores using electronic instruments, such as sensors, microcontrollers, and embedded systems, to measure displacement, force, strain, and temperature, which are crucial for detecting structural damage. Implementing intelligent technologies in SHM reduces the reliance on manual and hazardous inspection practices, simplifying and reducing the cost of building monitoring. The article highlights the social, economic, and environmental benefits of adopting intelligent technologies in SHM by presenting key findings from existing research. This review aims to increase the reader’s understanding of the significance of these technologies in enhancing the efficiency of SHM in civil engineering by illuminating their advancements and applications.

The Concept of Creating Digital Twins of Bridges Using Load Tests

“…Measurements carried out on the existing structure are compared on an ongoing basis with the results of the FEM simulation. Due to the scope, amount, and complexity of the data, this comparison is usually carried out using automatic or semi-automatic systems based on machine learning and artificial intelligence algorithms [57][58][59]. It may include damage recognition based on the computer vision [60,61], structural condition assessment by pattern recognition and detection of anomalies [62], surrogate model-based reliability analysis [63], and dynamic measurement of displacement [64].…”