Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

A statistical strategy to select optimal structural health monitoring data in operational modal analysis

Operational modal analysis (OMA) is prevalent in modal identifi cation of civil structures. It asks for response measurements of the underlying structure under ambient loads. A valid OMA method requires the excitation be white noise in time and space. Although there are numerous applications of OMA in the literature, few have investigated the statistical distribution of a measurement and the infl uence of such randomness to modal identifi cation. This research has attempted modifi ed kurtosis to evaluate the statistical distribution of raw measurement data. In addition, a windowing strategy employing this index has been proposed to select quality datasets. In order to demonstrate how the data selection strategy works, the ambient vibration measurements of a laboratory bridge model and a real cable-stayed bridge have been respectively considered. The analysis incorporated with frequency domain decomposition (FDD) as the target OMA approach for modal identifi cation. The modal identifi cation results using the data segments with different randomness have been compared. The discrepancy in FDD spectra of the results indicates that, in order to fulfi l the assumption of an OMA method, special care shall be taken in processing a long vibration measurement data. The proposed data selection strategy is easy-to-apply and verifi ed effective in modal analysis.

Damage assessment in beams and plates using variations in modal properties

Damage detection using modal properties is a widely accepted method. However, quantifying such damage using modal properties is still not well established. With this in mind, a research project is presently underway towards the development of a procedure to detect, locate and quantify damage in structural components using the variations in modal properties. A novel vibration based parameter called Vibration based Damage Index is introduced into the damage assessment procedure. This paper presents the early part of the research project which treats flexural members. The proposed procedure is validated using experimental data and/or theoretical techniques and illustrated through application. Outcomes of this research highlight the ability of the proposed procedure to successfully detect, locate and quantify damage in flexural structural components using the modal properties of the first few modes.

Use of modal flexibility method to detect damage in suspended cables and the effects of cable parameters

Modal flexibility is a widely accepted technique to detect structural damage using vibration characteristics. Its application to detect damage in long span large diameter cables such as those used in suspension bridge main cables has not received much attention. This paper uses the modal flexibility method incorporating two damage indices (DIs) based on lateral and vertical modes to localize damage in such cables. The competency of those DIs in damage detection is tested by the numerically obtained vibration characteristics of a suspended cable in both intact and damaged states. Three single damage cases and one multiple damage case are considered. The impact of random measurement noise in the modal data on the damage localization capability of these two DIs is next examined. Long span large diameter cables are characterized by the two critical cable parameters named bending stiffness and sag-extensibility. The influence of these parameters in the damage localization capability of the two DIs is evaluated by a parametric study with two single damage cases. Results confirm that the damage index based on lateral vibration modes has the ability to successfully detect and locate damage in suspended cables with 5% noise in modal data for a range of cable parameters. This simple approach therefore can be extended for timely damage detection in cables of suspension bridges and thereby enhance their service during their life spans.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Source location of acoustic emission waves for structural health monitoring of bridges

The process of structural health monitoring (SHM) involves monitoring a structure over a period of time using appropriate sensors, extracting damage sensitive features from the measurements made by the sensors and analysing these features to determine the current state of the structure. Various techniques are available for structural health monitoring of structures and acoustic emission (AE) is one technique that is finding an increasing use. Acoustic emission waves are the stress waves generated by the mechanical deformation of materials. AE waves produced inside a structure can be recorded by means of sensors attached on the surface. Analysis of these recorded signals can locate and assess the extent of damage. This paper describes preliminary studies on the application of AE technique for health monitoring of bridge structures. Crack initiation or structural damage will result in wave propagation in solid and this can take place in various forms. Propagation of these waves is likely to be affected by the dimensions, surface properties and shape of the specimen. This, in turn, will affect source localization. Various laboratory test results will be presented on source localization, using pencil lead break tests. The results from the tests can be expected to aid in enhancement of knowledge of acoustic emission process and development of effective bridge structure diagnostics system.

Using Fiber Bragg Grating (FBG) sensors to measure vertical displacements of bridges : a preliminary study

In many bridges, vertical displacements are the most relevant parameter for monitoring in the both short and long term. However, it is difficult to measure vertical displacements of bridges and yet they are among the most important indicators of structural behaviour. Therefore, it prompts a need to develop a simple, inexpensive and yet more practical method to measure vertical displacements of bridges. With the development of fiber-optics technologies, fiber Bragg grating (FBG) sensors have been widely used in structural health monitoring. The advantages of these sensors over the conventional sensors include multiplexing capabilities, high sample rate, small size and electro magnetic interference (EMI) immunity. In this paper, methods of vertical displacement measurements of bridges are first reviewed. Then, FBG technology is briefly introduced including principle, sensing system, characteristics and different types of FBG sensors. Finally, the methodology of vertical displacement measurements using FBG sensors is presented and a trial test is described. It is concluded that using FBG sensors is feasible to measure vertical displacements of bridges. This method can be used to understand global behaviour of bridge‘s span and can further develop for structural health monitoring techniques such as damage detection.

Performance characterization of VGCF/epoxy nanocomposite sensors under static load cycles and in static structural health monitoring

Compared to conventional metal-foil strain gauges, nanocomposite piezoresistive strain sensors have demonstrated high strain sensitivity and have been attracting increasing attention in recent years. To fulfil their ultimate success, the performance of vapor growth carbon fiber (VGCF)/epoxy nanocomposite strain sensors subjected to static cyclic loads was evaluated in this work. A strain-equivalent quantity (resistance change ratio) in cantilever beams with intentionally induced notches in bending was evaluated using the conventional metal-foil strain gauges and the VGCF/epoxy nanocomposite sensors. Compared to the metal-foil strain gauges, the nanocomposite sensors are much more sensitive to even slight structural damage. Therefore, it was confirmed that the signal stability, reproducibility, and durability of these nanocomposite sensors are very promising, leading to the present endeavor to apply them for static structural health monitoring.

Study of structural changes of pumpkin tissue before and after mechanical loading

The texture of agricultural crops changes during harvesting, post harvesting and processing stages due to different loading processes. There are different source of loading that deform agricultural crop tissues and these include impact, compression, and tension. Scanning Electron Microscope (SEM) method is a common way of analysing cellular changes of materials before and after these loading operations. This paper examines the structural changes of pumpkin peel and flesh tissues under mechanical loading. Compression and indentation tests were performed on peel and flesh samples. Samples structure were then fixed and dehydrated in order to capture the cellular changes under SEM. The results were compared with the images of normal peel and flesh tissues. The findings suggest that normal flesh tissue had bigger size cells, while the cellular arrangement of peel was smaller. Structural damage was clearly observed in tissue structure after compression and indentation. However, the damages that resulted from the flat end indenter was much more severe than that from the spherical end indenter and compression test. An integrated deformed tissue layer was observed in compressed tissue, while the indentation tests shaped a deformed area under the indenter and left the rest of the tissue unharmed. There was an obvious broken layer of cells on the walls of the hole after the flat end indentations, whereas the spherical indenter created a squashed layer all around the hole. Furthermore, the influence of loading was lower on peel samples in comparison with the flesh samples. The experiments have shown that the rate of damage on tissue under constant rate of loading is highly dependent on the shape of equipment. This fact and observed structural changes after loading underline the significance of deigning post harvesting equipments to reduce the rate of damage on agricultural crop tissues.

Damage identification and condition assessment of building structures using frequency response functions and neural networks

This thesis investigated the viability of using Frequency Response Functions in combination with Artificial Neural Network technique in damage assessment of building structures. The proposed approach can help overcome some of limitations associated with previously developed vibration based methods and assist in delivering more accurate and robust damage identification results. Excellent results are obtained for damage identification of the case studies proving that the proposed approach has been developed successfully.

Health monitoring of short-and medium-spen bridges using an improved modal strain energy method

Increasing the importance and use of infrastructures such as bridges, demands more effective structural health monitoring (SHM) systems. SHM has well addressed the damage detection issues through several methods such as modal strain energy (MSE). Many of the available MSE methods either have been validated for limited type of structures such as beams or their performance is not satisfactory. Therefore, it requires a further improvement and validation of them for different types of structures. In this study, an MSE method was mathematically improved to precisely quantify the structural damage at an early stage of formation. Initially, the MSE equation was accurately formulated considering the damaged stiffness and then it was used for derivation of a more accurate sensitivity matrix. Verification of the improved method was done through two plane structures: a steel truss bridge and a concrete frame bridge models that demonstrate the framework of a short- and medium-span of bridge samples. Two damage scenarios including single- and multiple-damage were considered to occur in each structure. Then, for each structure, both intact and damaged, modal analysis was performed using STRAND7. Effects of up to 5 per cent noise were also comprised. The simulated mode shapes and natural frequencies derived were then imported to a MATLAB code. The results indicate that the improved method converges fast and performs well in agreement with numerical assumptions with few computational cycles. In presence of some noise level, it performs quite well too. The findings of this study can be numerically extended to 2D infrastructures particularly short- and medium-span bridges to detect the damage and quantify it more accurately. The method is capable of providing a proper SHM that facilitates timely maintenance of bridges to minimise the possible loss of lives and properties.

Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods

This paper presents the results of a research project aimed at examining the capabilities and challenges of two distinct but not mutually exclusive approaches to in-service bridge assessment: visual inspection and installed monitoring systems. In this study, the intended functionality of both approaches was evaluated on its ability to identify potential structural damage and to provide decision-making support. Inspection and monitoring are compared in terms of their functional performance, cost, and barriers (real and perceived) to implementation. Both methods have strengths and weaknesses across the metrics analyzed, and it is likely that a hybrid evaluation technique that adopts both approaches will optimize efficiency of condition assessment and ultimately lead to better decision making.

The anatomical and biomechanical consequences of anterior vertebral stapling for the fusionless correction of scoliosis

Fusionless scoliosis surgery is an emerging treatment for idiopathic scoliosis as it offers theoretical advantages over current forms of treatment. Anterior vertebral stapling using a nitinol staple is one such treatment. Despite increasing interest in this technique, little is known about the effects on the spine following insertion, or the mechanism of action of the staple. The aims of this study were threefold; (1) to measure changes in the bending stiffness of a single motion segment following staple insertion, (2) to describe the forces that occur within the staple during spinal movement, and (3) to describe the anatomical changes that occur following staple insertion. Results suggest that staple insertion consistently decreased stiffness in all directions of motion. An explanation for the finding may be found in the outcomes of the strain gauge testing and micro-CT scan. The strain gauge testing showed that once inserted, the staple tips applied a baseline compressive force to the surrounding trabecular bone and vertebral end-plate. This finding would be consistent with the current belief that the clinical effect of the staples is via unilateral compression of the physis. Interestingly however, as each specimen progressed through the five cycles of each test, the baseline load on the staple tips gradually decreased, implying that the force at the staple tip-bone interface was decreasing. We believe that this was likely occurring as a result of structural damage to the trabecular bone and vertebral end-plate by the staple effectively causing ‘loosening’ of the staple. This hypothesis is further supported by the findings of the micro-CT scan. The pictures depict significant trabecular bone and physeal injury around the staple blades. These results suggest that the current hypothesis that stapling modulates growth through physeal compression may be incorrect, but rather the effect occurs through mechanical disruption of the vertebral growth plate.

Single-stranded DNA-binding protein hSSB1 is critical for genomic stability

Single-strand DNA (ssDNA)-binding proteins (SSBs) are ubiquitous and essential for a wide variety of DNA metabolic processes, including DNA replication, recombination, DNA damage detection and repair1. SSBs have multiple roles in binding and sequestering ssDNA, detecting DNA damage, stimulating nucleases, helicases and strand-exchange proteins, activating transcription and mediating protein–protein interactions. In eukaryotes, the major SSB, replication protein A (RPA), is a heterotrimer1. Here we describe a second human SSB (hSSB1), with a domain organization closer to the archaeal SSB than to RPA. Ataxia telangiectasia mutated (ATM) kinase phosphorylates hSSB1 in response to DNA double-strand breaks (DSBs). This phosphorylation event is required for DNA damage-induced stabilization of hSSB1. Upon induction of DNA damage, hSSB1 accumulates in the nucleus and forms distinct foci independent of cell-cycle phase. These foci co-localize with other known repair proteins. In contrast to RPA, hSSB1 does not localize to replication foci in S-phase cells and hSSB1 deficiency does not influence S-phase progression. Depletion of hSSB1 abrogates the cellular response to DSBs, including activation of ATM and phosphorylation of ATM targets after ionizing radiation. Cells deficient in hSSB1 exhibit increased radiosensitivity, defective checkpoint activation and enhanced genomic instability coupled with a diminished capacity for DNA repair. These findings establish that hSSB1 influences diverse endpoints in the cellular DNA damage response.