Papers by Keyword: Damage Identification

Abstract: Safety and sustainability of reinforced concrete bridges may be increased by observing their condition during operation and thus accurately predicting their behaviour under various load conditions. This can be achieved through a monitoring system and automatic error detection based on the measured data. By detecting potential issues early on, significant damages can be prevented before they occur. Despite extensive data collection from many monitored bridges, this data often remains unprocessed and uninformative in its raw form. We aim to transform this data into a format that can help to estimate a bridge’s health condition. This approach is presented through a case study of an existing reinforced concrete box girder bridge in Hungary. Digital twin (DT) technology was used to simulate the bridge’s behaviour and to verify structural conditions under any given traffic load arrangement. Static calculations and verification of load-bearing and serviceability conditions were performed on a validated 3D finite element (FE) model. Different traffic load scenarios were randomly generated using Monte Carlo simulation, and the bridge’s condition was evaluated for each case. The actual condition was quantified by parameters such as the bridge’s utilization for different USL and SLS limit values, especially for deflection and crack width. In the FE model, the physical characteristics that are recorded on the real bridge by the actual measuring instruments were also recorded at the locations corresponding to the monitoring points on the actual structure. The relationship between the virtual bridge’s condition and the virtual monitoring data was determined using artificial intelligence (AI) applications, particularly artificial neural networks (ANN). Based on this relationship, the monitoring data measured on the real bridge can be processed, and predictions about the bridge’s actual condition can be made to support maintenance and improve the safety and sustainability of the structure. This approach demonstrates the potential of DT and AI in structural health monitoring techniques.

Abstract: Guided-waves-based diagnostic imaging techniques have been attracting much attention due to their merits including easily interpretable image, high identification accuracy and suitable for online surveillance. In this study, to envisage the difficulty in detecting orientation-specific damage (crack, notch etc.), a novel guided-waves-based diagnostic imaging technique capable of inspecting complicated engineering structures was developed, in terms of the relationship between damage parameters (location, orientation and severity) and extracted guided waves signal features (time-of-flight, signal correlation and signal energy). Experimental studies were performed to verify the developed diagnostic imaging approach, where a through-thickness crack was successfully identified in a metallic plate and a part of real rail structure respectively.

Abstract: BP neural network is introduced and applied to identify and diagnose both location and extent of bridge structural damage; static load tests and dynamic calculations are also made on bridge structural damage behind abutment. The key step of this method is to design a reasonably perfect BP network model. According to the current knowledge, three BP neural networks are designed with horizontal displacement rate and inherent frequency rate as damage identification indexes. The neural networks are used to identify the measurement of structure behind abutment and the calculation of damage location and extent, at the same time, they can also be used to compare and analyze the results. The test results show that: taking the two factors (static structural deformation rate and the change rate of natural frequency in dynamic response) as input vector, the BP neural network can accurately identify the damage location and extent, implying a promising perspective for future applications.

Abstract: In this work a methodology for effective positioning of sensors and actuators for damage detection and characterisation is described. The novelty of the proposed methodology is that the fitness function to be optimised does not contain probability of detection (POD) which needs to be obtained for every possible sensor combination. The proposed fitness function is to provide the maximum coverage of the structure via Lamb waves and reduce the negative effects of boundary reflections. Once the fitness function is defines, genetic algorithm (GA) is used as an optimisation strategy to result in optimal sensor positioning.

Abstract: Structural Health Monitoring (SHM) techniques have gained an increased interest to be utilised alongside NDI techniques for aircraft maintenance. However, to take the SHM methodologies from the laboratory conditions to actual structures under real load conditions requires them to be assessed in terms of reliability and robustness. In this work, a statistical analysis is carried out for an SHM system for damage detection and characterisation in composite structures. The sensitivity of the platform to parameters such as noise, sensor failure, placement tolerances and bonding has been investigated and reported.

Abstract: In the present study, a detailed vehicle–bridge dynamic interaction model is established, and the bridge is modeled as laminated composite beams which are discretized as finite beam elements. The vehicle-induced responses of the bridge in the damaged state are used as input data for damage identification and the response sensitivities with respect to the damage indices of the elements are calculated to establish the sensitivity matrix. Based on the error between the measured response and the computed one as a minimization criterion, the sensitivity equation is solved by the least-squares method, and then the damage is located and quantified with the finite element model updating technique. It can be concluded that only one measurement point is required to detect the damage of the bridge, and location of the measurement point does not significantly affect the identification result. Furthermore, it is noted that the absolute damage of any beam element is well identified by using either the displacement response, velocity response or acceleration response.

Abstract: Based on the characteristics of space truss structures, the concept of modal strain energy is introduced and square difference in elemental modal strain is presented. Through the square difference in elemental modal strain and wavelet transform, this paper presents a method for space truss structure damage recognition. The structural damage index is presented with the change of wavelet coefficients. Numerical simulation results show that: this method is effective to locate the single, multiple damages and light, severe damage with the first mode information. The preliminary tries for damage extent identification were made by the wavelet coefficients.

Abstract: A method based on the square difference of elemental modal strain was proposed to determine the damage location and damage degree. The square difference of elemental modal strain was expressed damage before and after. Simulation results show that: this method is effective to locate the single, multiple damages and light, severe damage with low-order modal information. Damage degrees can be initially determined by the values of the square difference of damaged-element modal strain. Furthermore, the result also shows that this method can accurately identify the damage location of plane truss structures with noise.

Abstract: A damage identification method is proposed to identify the damage style and the damage parameters. By driving a pair of PZT patches out phase and in phase, the electric admittance of the PZT is obtained. The damage parameters are then identified from the changes of the admittance spectra caused by the appearance of damage. By comparing the identification result, the damage style can be determined and the damage parameters can be obtained. The middle basic particle swarm optimization algorithm is employed as a global search technique to back-calculate the damage. Experiments are carried out on beams. The results demonstrate that the proposed method is able to identify the damage style, and can effectively and reliably locate and quantify the damage in the beam.

Abstract: Tensile test of low carbon steel was carried out on a universal electronic testing machine. Loading and strain test data of low carbon steel was acquired by means of the tensile test. Based on the measured tensile specimen data, elastic modulus of low carbon steel was calculated. It was found that elastic modulus of low carbon steel varied during tensile process. The damage of low carbon steel under tensile was identified by the calculation of elastic modulus.