1,720,963 research outputs found
Vehicle-assisted bridge damage assessment using deep learning
No full textThis thesis introduces innovative methodologies for vehicle-assisted bridge health monitoring, aiming to improve maintenance procedures of ageing infrastructure, a critical concern for transport network owners. By taking advantage of advancements in sensing technology and the increasing interconnectivity between vehicles and infrastructure, these methodologies focus on developing an automated bridge assessment method that efficiently evaluates the current condition of bridge structures. This approach enables more accurate and timely maintenance decisions.
The primary objective of this thesis is to create an automated bridge assessment framework for existing bridges by harnessing the synergy between sensors installed on structures and signals transmitted by passing vehicles. By gathering comprehensive information from various sources, including vehicles and the bridge itself, and fusing this data using deep learning techniques, the framework efficiently evaluates the current condition of bridge structures, facilitating more precise and prompt maintenance decisions.
The thesis comprises several studies investigating deep learning techniques, such as deep autoencoders (DAE) and probabilistic temporal autoencoders (PTAE), for extracting features and capturing temporal relationships in the data. This enables accurate identification and quantification of potential damage in bridge structures.
The first study (Paper IA IB) examines an indirect bridge monitoring system using vertical acceleration responses from a fleet of vehicles passing over a healthy bridge. This study’s findings reveal that the error in signal reconstruction from the trained DAE is sensitive to damage, considering the distribution of results from multiple separate vehicle-crossing events. The proposed method proves effective in detecting damage under operational conditions and demonstrates potential as a new tool for cost-effective bridge health monitoring.
The second study introduces a methodology for assessing bridge conditions using a PTAE and multi-sensor data from a fixed sensing framework, collected during train crossings. The study’s results indicate that the proposed method can detect damage with a limited number of sensors, making it a valuable approach to enhance bridge safety. An Exponentially Weighted Moving Average (EWMA) filter and a control chartbased threshold mechanism are applied to further refine the damage assessment process, distinguishing between healthy and progressively deteriorating damage cases.
The third study proposes a Probabilistic Deep Neural Network framework for damage assessment, combining vehicle and bridge responses to extract damage-sensitive features for classifying different damage states. The findings of this study demonstrate that incorporating multiple sensor information reduces uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions.
In conclusion, this thesis advances knowledge in the field of structural assessment through structural health monitoring by providing insights and improvements in techniques and methodologies. By taking advantage of the combined strengths of sensors mounted on structures and signals transmitted by moving vehicles, the developed methodologies provide reliable and precise damage evaluation capabilities. These valuable insights enhance bridge safety, improve resource allocation, and contribute to the overall performance of transport networks. Ultimately, this approach leads to more sustainable and resilient infrastructure, better equipped to handle modern society’s growing demands
Multimetric Event-driven System for Long-Term Wireless Sensor Operation in SHM Application
No full textWireless sensor networks (WSNs) are promising solutions for large infrastructure monitoring because of their ease of installation, computing and communication capability, and cost-effectiveness. Long-term Civil structural health monitoring (SHM), however, is still a challenge because it requires continuous data acquisition for the detection of random events such as earthquakes and structural collapse. To achieve long-term operation, it is necessary to reduce the power consumption of sensor nodes designed to capture random events and, thus, enhance structural safety. In this paper, we present an event-based sensing system design based on an ultra-low-power microcontroller with programmable event-detection mechanism to allow continuous monitoring; the device is triggered by vibration, strain, or a timer and has a programmed threshold, resulting in ultra-low-power consumption of the sensor node. Furthermore, the proposed system can be easily reconfigured to any existing wireless sensor platform to enable ultra-low power operation. For validation, the proposed system was integrated with a commercial wireless platform to allow strain, acceleration, and time-based triggering with programmed thresholds and current consumptions of 7.43 and 0.85 mA in active and inactive modes, respectively
Multimetric Event-driven System for Long-Term Wireless Sensor Operation in SHM Application
Full text linkWireless sensor networks (WSNs) are promising solutions for large infrastructure monitoring because of their ease of installation, computing and communication capability, and cost-effectiveness. Long-term Civil structural health monitoring (SHM), however, is still a challenge because it requires continuous data acquisition for the detection of random events such as earthquakes and structural collapse. To achieve long-term operation, it is necessary to reduce the power consumption of sensor nodes designed to capture random events and, thus, enhance structural safety. In this paper, we present an event-based sensing system design based on an ultra-low-power microcontroller with programmable event-detection mechanism to allow continuous monitoring; the device is triggered by vibration, strain, or a timer and has a programmed threshold, resulting in ultra-low-power consumption of the sensor node. Furthermore, the proposed system can be easily reconfigured to any existing wireless sensor platform to enable ultra-low power operation. For validation, the proposed system was integrated with a commercial wireless platform to allow strain, acceleration, and time-based triggering with programmed thresholds and current consumptions of 7.43 and 0.85 mA in active and inactive modes, respectively.acceptedVersion© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Vehicle assisted bridge damage assessment using probabilistic deep learning
No full textVehicle assisted monitoring has shown promising potential for the condition assessment of existing bridges in a road network, by removing practical complications faced in traditional Structural health monitoring (SHM) methods such as traffic interruption and dense deployment of sensors. However, the combination of different measurement sources during vehicle assisted monitoring has not yet been fully explored. This paper aims to evaluate the potential benefit of considering multiple measured responses from various sources, including fixed sensors on the bridge and on-board vehicle sensors. To this end, this paper proposes a Probabilistic Deep Neural Network, a stochastic data-driven framework for damage assessment. This framework enables the combination of vehicle and bridge responses to extract damage sensitive features for the classification of different damage states. In addition, the proposed method estimates the uncertainty of its predictions, providing an indication of the reliability of the result. The proposed method is validated using two numerical based case studies while considering realistic operational conditions, which include temperature oscillations, additional traffic, and measurement noise. The results from this study indicate that combining multiple sensor information results in lower uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions.publishedVersio
Deep autoencoder architecture for bridge damage assessment using responses from several vehicles
No full textVehicle-assisted monitoring is a promising alternative for rapid and low-cost bridge health monitoring compared to direct instrumentation of bridges. In recent years, centralized management systems for fleets of heavy vehicles have been adopted in transportation networks for logistics and other applications. These vehicles can be instrumented and easily integrated with the existing fleet management systems to provide information that can be used for bridge health monitoring. In this study, a numerical investigation is carried out to evaluate the feasibility of an indirect bridge monitoring system considering responses from several vehicles under operational conditions. The proposed method uses the vertical acceleration responses from a fleet of vehicles passing over a healthy bridge to train a deep autoencoder model (DAE) for bridge damage sensitive features. It is shown that the error in signal reconstruction from the trained DAE is sensitive to damage, when considering the distribution or results from several separate vehicle-crossing events. The bridge damage is quantified with a damage index based on the Kullback-Leibler divergence that evaluates the change in the distributions of the reconstruction errors. The performance of the proposed method is evaluated for two numerical scenarios of vehicle populations, for different damage cases including the effect of operational uncertainties (road profile, measurement noise, and variability in vehicle properties). The proposed method is also evaluated for more realistic multi-span continuous bridge for different damage cases in the presence of random traffic. The result show that the proposed method can detect damage under operational conditions and that it has the potential to become a new tool for cost-effective bridge health monitoring
Probabilistic autoencoder-based bridge damage assessment using train-induced responses
Full text linkStructural health monitoring (SHM) systems have been increasingly employed to continually assess the current state of bridges. However, the vast amounts of sensor data generated by SHM systems, along with constantly changing environmental and operational conditions, make structural damage assessment a computationally demanding and challenging task. Traditional data-driven approaches primarily utilise machine learning methods for pattern recognition and feature extraction to address this issue. This paper introduces a methodology for assessing bridge conditions using a probabilistic temporal autoencoder (PTAE). The proposed approach effectively extracts features and captures temporal relationships in multi-sensor data collected only during train crossings. By calculating the reconstruction loss and KL divergence-based of damage features, the methodology enables the identification of potential damage of a monitored bridge. An Exponentially Weighted Moving Average (EWMA) filter and a control chart-based threshold mechanism are applied to further refine the damage assessment process, facilitating the distinction between healthy and progressively deteriorating damage cases. The proposed method is adaptable to various monitoring scenarios and sensor configurations, and robust to varying operational and environmental conditions. The effectiveness of the methodology is assessed using numerically generated data and validated with real-world data from the KW51 bridge. The results demonstrate that the proposed method can detect damage with a limited number of sensors, making it a valuable approach to enhance bridge safety.publishedVersio
Bridge Displacement Estimation Using a Co-Located Acceleration and Strain
No full textStructural displacement is an important metric for assessing structural conditions because it has a direct relationship with the structural stiffness. Many bridge displacement measurement techniques have been developed, but most methods require fixed reference points in the vicinity of the target structure that limits the field implementations. A promising alternative is to use reference-free measurement techniques that indirectly estimate the displacement by using measurements such as acceleration and strain. This paper proposes novel reference-free bridge displacement estimation by the fusion of single acceleration with pseudo-static displacement derived from co-located strain measurements. First, we propose a conversion of the strain at the center of a beam into displacement based on the geometric relationship between strain and deflection curves with reference-free calibration. Second, an adaptive Kalman filter is proposed to fuse the displacement generated by strain with acceleration by recursively estimating the noise covariance of displacement from strain measurements which is vulnerable to measurement condition. Both numerical and experimental validations are presented to demonstrate the efficiency and robustness of the proposed approach
Vehicle assisted bridge damage assessment using probabilistic deep learning
No full textVehicle assisted monitoring has shown promising potential for the condition assessment of existing bridges in a road network, by removing practical complications faced in traditional Structural health monitoring (SHM) methods such as traffic interruption and dense deployment of sensors. However, the combination of different measurement sources during vehicle assisted monitoring has not yet been fully explored. This paper aims to evaluate the potential benefit of considering multiple measured responses from various sources, including fixed sensors on the bridge and on-board vehicle sensors. To this end, this paper proposes a Probabilistic Deep Neural Network, a stochastic data-driven framework for damage assessment. This framework enables the combination of vehicle and bridge responses to extract damage sensitive features for the classification of different damage states. In addition, the proposed method estimates the uncertainty of its predictions, providing an indication of the reliability of the result. The proposed method is validated using two numerical based case studies while considering realistic operational conditions, which include temperature oscillations, additional traffic, and measurement noise. The results from this study indicate that combining multiple sensor information results in lower uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
