The artificial neural network (ANN) is a classical ML method and has been applied to civil engineering since 1989 (Adeli and Yeh 1989). As a branch of artificial intelligence, ML aims to develop trainable algorithms to learn from data, based on which predictions can be made (Pan et al 2017 Pan et al 2018). The appearance of machine learning (ML) provides a possible solution for the troubles mentioned above. The CV technologies usually face disturbances caused by light, distortion, weather, and occlusion in the outdoor environment. Moreover, computer vision (CV) technologies are also used to detect local damage, such as cracks, spalling, delamination, and rust, and to extract global information, like displacement, acceleration, loads, from images or videos captured by cameras. Data-driven methods regard the mission as a statistical pattern recognition problem (Farrar and Worden 2012) and have been applied substantially, but their complexity and computation requirements are generally of polynomial order concerning data size (Sun et al 2020). However, it is a hard task due to simplifying assumptions when modeling bridge structures and uncertainties of material and geometric properties (Sun et al 2020).
#BRIDGE DIAGNOSTICS REVIEWS UPDATE#
The former attempts to update the finite-element model (FEM) of the undamaged bridge in terms of some key parameters against the measurement data, and the differences between its predictions and the measurements indicates the existence of damages (Xiao et al 2015 Zhu et al 2015). The methods developed for analyzing the monitoring data can be distinguished into two categories: model-based methods and data-driven methods (Sun et al 2020). Analyzing the accumulated monitoring data to realize SHM has naturally become the priority of SHM research. SHM lies in sensing and communication technologies, and the recent advancements in both technologies provide chances to acquire monitoring data at an unprecedented speed and amount. Therefore, structural health monitoring (SHM) systems are developed and installed on some bridges with the aims to timely find structural damage or degradation (Housner et al 1997). Nevertheless, the visual inspection is labor-intensive, time-consuming, subjective, and hard to reflect real structure condition alteration in time (Sun et al 2020). Traditionally, visual inspection conducted by experienced inspectors is the main method adopted for this mission (Xu and Xia 2012). Monitoring the bridge condition and detecting their damages are essential to ensure their serviceability and safety.
#BRIDGE DIAGNOSTICS REVIEWS PRO#
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