Deep learning-based indirect bridge damage identification system

Abstract

With the growing number of well-aged bridges and the urgency in developing reliable, (pseudo-) real-time monitoring of structural safety and integrity, there is a worldwide and widespread campaign toward transforming structural health monitoring practice. Among these attempts, the application of data-driven approaches in developing damage identification techniques has received particular attention in recent years. Given the growing volume of structural health monitoring data, the power of data-driven approaches has been further exploited. These efforts have been predominantly focused on building and training algorithms using direct measurements from bridges. Although recent years have seen transformative technologies in producing cheap and wireless sensors, network-wide bridge instrumentation is logistically difficult and expensive. This has led to a new group of structural health monitoring systems entitled indirect or drive-by approaches. In drive-by systems, measurements from an instrumented vehicle are used to extract structural damage signatures. In other words, in these systems, the instrumented vehicle acts as both actuator and receiver while passing over a bridge. The main challenge in deploying drive-by approaches for damage identification purposes is that the signals collected on drive-by vehicles also embody signatures from the vehicle, road/rail profile and are easily contaminated by environmental and operational conditions. Furthermore, the majority of current drive-by damage identification systems rely on prior knowledge of vehicle or bridge dynamic characteristics which has led to limited application of the concept in practice so far. To address these challenges, this study employs a powerful class of deep learning algorithm to develop a damage identification system using measurements on an instrumented travelling train. The proposed algorithm is capable of automatically extracting damage signatures from train-borne measurements only. To demonstrate the algorithm’s capability, the method is applied to measurements collected on a model instrumented train travelling on a simply supported model steel bridge. For this purpose, a deep convolutional neural network is built, optimised, trained and tested to detect damage using acceleration signals collected on the instrumented train only. The hyperparameters of the algorithm are optimised using the Bayesian optimisation technique. The accuracy of the algorithm is experimentally tested for four positive damage scenarios (combination of two different locations and intensity) and three different travelling speeds. This is the first demonstration of the data-driven drive-by damage identification system under scaled operational environment conditions. The performance of the proposed method is discussed under different travelling speeds and different damage states. The result shows that the proposed method can accurately and automatically detect and classify damage under varying speed, rail irregularities and operational noise using train-borne measurements only and offers a great promise in transforming the future of bridge damage identification system.

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

“…Meanwhile, deep learning is one approach to machine learning and AI that models how the human brain acquires knowledge and excels at processing massive datasets. One of Deep Learning's most valuable features is that, as more data is supplied, the model's performance improves [145,146]. Automatic actions are also included in the modeling processes and feature extraction.…”

Theoretical and Experimental Investigations of Identifying Bridge Damage Using Instantaneous Amplitude Squared Extracted from Vibration Responses of a Two-Axle Passing Vehicle

“…This is because the size of the damage is equated to the element size for modeling damage through element stiffness loss. In this study, the chosen size for the damaged element is 0.5 m, a dimension commonly employed in the literature [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Consequently, a total of 50 elements constitute the 25 m simply supported beam.…”

“…However, the application of the VSM in identifying bridge modal shapes and damage is primarily limited to theoretical investigations [15][16][17][18]. More recently, some laboratory tests on scaled VBI systems have attempted to fill this gap for validating the VSM in identifying bridge mode shapes and damage, but they are often conducted under ideal conditions [19][20][21][22]. The significant challenge in identifying bridge modal properties using the VSM arises from extracting weak bridge vibrational signals from vehicle responses amid the pollution caused by vehicle-induced vibrations and road surface roughness.…”

“…In the exploration of applying the VSM for indirect bridge damage identification, various studies have examined algorithms such as mode shape difference, wavelet transform, and deep learning [19][20][21]27]. Zhang et al [26] introduced the metric of instantaneous amplitude squared (IAS), derived from the Hilbert transform of isolated bridge vibration signals from vehicle responses, to detect bridge damage.…”

A deep‐learning framework for classifying the type, location, and severity of bridge damage using drive‐by measurements

“…The continuous wavelet transform map of the simulated on‐train vibrations was used to train the CNN with data from a series of different damage cases. The other paper by Hajializadeh (2022b) adopted a similar CNN‐based algorithm where real measurements from a scaled model of a train and bridge were used as inputs to the CNN. Images of the time‐frequency spectrograms were adopted as training inputs for the CNN.…”