Vibration-based damage detection for timber structures in Australia

© 2014 by Nova Science Publishers, Inc. All rights reserved. The use of non-destructive assessment techniques for evaluating structural conditions of aging infrastructure, such as timber bridges, utility poles and buildings, for the past 20 years has faced increasing challenges as a result of poor maintenance and inadequate funding. Replacement of structures, such as an old bridge, is neither viable nor sustainable in many circumstances. Hence, there is an urgent need to develop and utilize state-of-the-art techniques to assess and evaluate the ?health state? of existing infrastructure and to be able to understand and quantify the effects of degradation with regard to public safety. This paper presents an overview of research work carried out by the authors in developing and implementing several vibration methods for evaluation of damage in timber bridges and utility poles. The technique of detecting damage involved the use of vibration methods, namely damage index method, which also incorporated artificial neural networks for timber bridges and time-based non-destructive evaluation (NDE) methods for timber utility poles. The projects involved successful numerical modeling and good experimental validation for the proposed vibration methods to detect damage for simple beams subjected to single and multiple damage scenarios and was then extended to a scaled timber bridge constructed under laboratory conditions. The time-based NDE methods also showed promising trends for detecting the embedded depth and condition of timber utility poles in early stages of that research

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works

Optimal sensor placement in structural health monitoring (SHM) with a field application on a RC bridge

Structural health monitoring (SHM) is a research field that targets detecting and locating damage in structures. The main objective of SHM is to detect damage at its onset and inform authorities about the type, nature and location of the damage in the structure. Successful SHM requires deploying optimal sensor networks. We present a probabilistic approach to identify optimal location of sensors based on a priori knowledge on damage locations while considering the need for redundancy in sensor networks. The optimal number of sensors is identified using a multi-objective optimization approach incorporating information entropy and cost of the sensor network. As the size of the structure grows, the advantage of the optimal sensor network in damage detection becomes obvious. We also present an innovative field application of SHM using Field Programmable Gate Array (FPGA) and wireless communication technologies. The new SHM system was installed to monitor a reinforced concrete (RC) bridge on interstate I-40 in Tucumcari, New Mexico. The new monitoring system is powered with renewable solar energy. The integration of FPGA and photovoltaic technologies make it possible to remotely monitor infrastructure with limited access to power. Using calibrated finite element (FE) model of the bridge with real data collected from the sensors installed on the bridge, we establish fuzzy sets describing different damage states of the bridge. Unknown states of the bridge performance are then identified using degree of similarity between these fuzzy sets. The proposed SHM system will reduce human intervention significantly and can save millions of dollars currently spent on prescheduled inspection by enabling performance based monitoring

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Symmetry in Structural Health Monitoring

In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment

Reliability-Calibrated ANN-Based Load and Resistance Factor Load Rating for Steel Girder Bridges

This research aimed to develop a supplemental ANN-based tool to support the Nebraska Department of Transportation (NDOT) in optimizing bridge management investments when choosing between refined modeling, field testing, retrofitting, or bridge replacement. ANNs require an initial investment to collect data and train a network, but offer future benefits of speed and accessibility to engineers utilizing the trained ANN in the future. As the population of rural bridges in the Midwest approaching the end of their design service lives increases, Departments of Transportation are under mounting pressure to balance safety of the traveling public with fiscal constraints. While it is well-documented that standard code-based evaluation methods tend to conservatively overestimate live load distributions, alternate methods of capturing more accurate live load distributions, such as finite element modeling and diagnostic field testing, are not fiscally justified for broad implementation across bridge inventories. Meanwhile, ANNs trained using comprehensive, representative data are broadly applicable across the bridge population represented by the training data. The ANN tool developed in this research will allow NDOT engineers to predict critical girder distribution factors (GDFs), removing unnecessary conservativism from approximate AASHTO GDFs, potentially justifying load posting removal for existing bridges, and enabling more optimized design for new construction, using ten readily available parameters, such as bridge span, girder spacing, and deck thickness. A key drawback obstructing implementation of ANNs in bridge rating and design is the potential for unconservative ANN predictions. This research provides a framework to account for increased live load effect uncertainty incurred from neural network prediction errors by performing a reliability calibration philosophically consistent with AASHTO Load and Resistance Factor Rating. The study included detailed FEA for 174 simple span, steel girder bridges with concrete decks. Subsets of 163 and 161 bridges within these available cases comprised the ANN design and training datasets for critical moment and shear live load effects, respectively. The reliability calibration found that the ANN live load effect prediction error with mean absolute independent testing error of 3.65% could be safely accommodated by increasing the live load factor by less than 0.05. The study also demonstrates application of the neural network model validated with a diagnostic field test, including discussion of potential adjustments to account for noncomposite bridge capacity and Load Factor Rating instead of Load and Resistance Factor Rating. Advisor: Joshua S. Steelma

Temperature-Driven Structural Identification for Bridge Performance Evaluation

Bridges serve as integral components of infrastructure all around the world. Their direct impact to society is substantial, and their reliability is paramount. As such, confidence in the integrity of these structures is important not only for individuals who utilize these structures but also for the bridge owners and engineers who operate and maintain them. In order to develop a comprehensive understanding of the structural behavior, evaluations are conducted to assess the structure’s performance. By utilizing input-output relationships between loads and responses, structural performance evaluations provide an opportunity to assess unique bridge behavior such as complex mechanisms or deterioration. The research presented herein investigates a novel, temperature-driven concept for bridge performance evaluation wherein thermal behavior in response to environmental temperature changes is used to assess the structure. Within this research, two bridges are evaluated using a probabilistic approach of single and multiple model updating within the temperature-driven structural identification process. This technique utilizes Latin Hypercube Sampling as well as Bayesian calibration to identify unknown bridge parameters and evaluate the structural performance. Then, these studies are compiled into a synthesis of temperature-driven evaluations from nineteen bridge studies throughout the world to develop a comprehensive framework and to provide guidance for using thermal behavior for performance evaluations. The intellectual merit from each study illuminates various motivations, methods, successes, and challenges of temperature-driven evaluations. Guidance regarding structure details, monitoring criteria, as well as data and analysis is provided to assist bridge owners, engineers, and researchers who utilize this temperature-driven technique to conduct evaluations. Based on the research presented herein, temperature-driven performance evaluations provide extensive insight, not only to the thermal behavior of the bridge, but the overall structural health