Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: A systematic review

Niyirora, Rosette; Ji, Wei; Masengesho, Elyse; Munyaneza, Jean; Niyonyungu, Ferdinand; Nyirandayisabye, Ritha

doi:10.1016/j.rineng.2022.100761

Cited by 47 publications

(9 citation statements)

References 64 publications

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“…4. Compute the logarithms of the powers (sum of the products of triangular flters and power spectrum) at each Mel-scale frequency using the following equation (7):…”

Section: Structural Control and Health Monitoringmentioning

confidence: 99%

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Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Talebi-Kalaleh,

Mei

2024

Structural Control and Health Monitoring

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Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.

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“…4. Compute the logarithms of the powers (sum of the products of triangular flters and power spectrum) at each Mel-scale frequency using the following equation (7):…”

Section: Structural Control and Health Monitoringmentioning

confidence: 99%

“…Traditional bridge health monitoring techniques typically involve the installation of sensors directly on the structure, with subsequent analysis of the collected data to detect damage [7][8][9][10][11]. Recent research on fxed-sensorbased damage detection has emphasized Bayesian methods.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Talebi-Kalaleh,

Mei

2024

Structural Control and Health Monitoring

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“…Table 1 summarizes the existing literature reviews on the deployment of machine learning algorithms in SHM of bridges [50][51][52][53][54][55][56]. It can be observed from Table 1 that existing reviews describe the current trends in the field with different focus aspects.…”

Section: Existing Literature Reviews On the Deployment Of Machine Lea...mentioning

confidence: 99%

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Sonbul

Rashid

2023

Sensors

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Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements.

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“…Over time, the deficiencies inherent in these conventional methodologies, such as their absence of instantaneous monitoring, lack of data-driven fault detection, and their incapacity to identify subtle anomalies, become conspicuously apparent [6]. The current research trend is integrating artificial intelligence and big data in improving various early detections of any machine failure and large scale HVAC system [7,8].…”

Section: Introductionmentioning

confidence: 99%

Z-Freq Hybrid: Signal Analysis Based Car Air Conditioning Compressor Monitoring Technique Using Accelerometer and Piezo-Film Sensor

Othman,

Nuawi,

Ngatiman

et al. 2024

J. Phys.: Conf. Ser.

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The efficiency and performance of car air conditioning systems rely heavily on adequately functioning the compressor and its associated components. There are many ways to detect an unfunctioning compressor and one of them is through vibration. Disfunctioning compressors could lead to discomfort, fatigue, stress and fogging windows, especially in long-distance driving. Thus, researchers focus on detecting the nonfunctioning car air conditioning compressor early. This paper introduced a new enhanced statistical method, namely Z-freq Hybrid. Z-freq Hybrid was based on a Z-notch frequency domain filter with a combination input of two different types of sensors introduced to detect functioning compressors. Data were recorded at various compressor speeds using an accelerometer and piezo film sensor with Signal Express 2015 software. The acceleration (m/s2) and voltage (mV) data were analyzed to find the combination degree of scattering data in a Z-freq Hybrid chart. The analyzed data show that the Z-freq Hybrid coefficient increases as the compressor’s speed increases. Then, the value dropped significantly when the compressor was dysfunctional. In conclusion, a Z-freq Hybrid can be employed to detect abnormalities and irregular vibration patterns, which may indicate the impending failure of a compressor.

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Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: A systematic review

Cited by 47 publications

References 64 publications

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Z-Freq Hybrid: Signal Analysis Based Car Air Conditioning Compressor Monitoring Technique Using Accelerometer and Piezo-Film Sensor

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