Neural network based damage detection using substructure technique

Many researchers have been studying the feasibility of using Artificial Neural Networks (ANN) in structural health monitoring and damage detection. It has been proven by both numerical simulation and laboratory test data that ANN can give reliable prediction of structural conditions. The main drawback of using ANN in structural condition monitoring is the requirement of enormous computational effort. Consequently almost all the previous work described in the literature limited the structural members to a small number of large elements in the ANN model. This may result in the ANN model being insensitive to local damage, especially when this local damage is small. To overcome this problem, this study presents an approach to detect small structural damage by using ANN progressively. It uses the substructure technique together with a two-stage ANN to detect the location and extent of the damage. It starts by dividing the structure into a few substructures. The condition of each substructure is examined. Those substructures with condition change identified are further subdivided and their condition examined. By doing this progressively, the location and severity of low level structural damage can be detected. Modal parameters such as frequencies and mode shapes are used as the input to the ANN. To demonstrate the effectiveness of this approach, a two-span continuous concrete slab structure is used as an example. Different damage scenarios are introduced by reducing the local stiffness of the selected elements at different locations along the structure. The results show that this technique successfully detects simulated damage in the structure

Uncertainties Consideration in Empirical Frequency Response Function Data for Damage Identification Based On Artificial Neural Network

The modern application of frequency response function (FRF) with artificial neural networks (ANN) has become one of the leading methods in vibration-based damage detection approach. However, since full-size empirically obtained FRF data is used as ANN input, a broad composition ANN input layer series would occur. Consequently, principal component analysis (PCA) is adopted to compress the FRF data magnitude. Despite this, PCA alone is unable to select the important FRF data features effectively, due to the exceedingly FRF data size in addition with existing uncertainties. Therefore, this study proposed the merger of a non-probabilistic analysis and ANN approach with PCA by considering the uncertainties effect and the inefficiency of using empirical FRF data. The empirical FRF data is obtained from a steel truss bridge structure. The results show that the PoDE values above 95% are measured at the particular executed damage locations and the DMI values show the damage severity at the actual damage locations. Overall, the results show that the proposed method is capable in considering the uncertainties effect on the empirical FRF data for structural damage identification

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

Statistical vibration based damage identification using artificial neural network

Artificial Neural Network (ANN) has been widely applied to detect damages in structures based on structural vibration modal parameters. However, uncertainties that inevitably exist in finite element model and measured vibration data might lead to false or unreliable prediction of structural damage. In this study, a statistical approach is proposed to include the effect of uncertainties in the ANN algorithm for damage prediction. ANN is used to predict the stiffness parameters of structures from measured structural vibration frequencies and mode shapes. Uncertainties in the measured data and finite element model of the structure are considered in the prediction. The statistics of the identified parameters are determined using Rossenblueth’s point estimation method and verified by Monte Carlo simulation. The results show that by considering these uncertainties in the ANN model, the damages can be detected with a higher confidence level

Vibration-based damage detection of slab structure using artificial neural network

This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration-based data has been used as the input to ANN for location and severity prediction of damages in a slab-like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure

Detection of concrete spalling using changes in modal flexibility

This paper presents a study in the effect of spalling to dynamic parameters such as natural frequencies and mode shapes. Numerical example of a slab is used as an example in this study. The slab will be modelled using ANSYS 11.0 and various types of spalling are imposed. The changes of vibration parameters are monitored and compared. To compare the sensitivity of modal parameters to spalling is determined using the flexibility method. Based on the results it is found that by incorporating mode shapes using flexibility method, damage location and severity can be obtained

Application of artificial neural network in bridge deck condition rating

Currently bridges are evaluated either a visual inspection process or structural analysis. When bridge evaluation is conducted by visual inspection a subjective rating is assigned to the bridge components with analytical evaluation. The rating is computed based on the load applied and the resistance capacity of bridge component visual inspection is subjective and depends primarily on the experience of the inspector in assigning the rating. analytical rating unable to represent the condition of bridge component since the rating is computed based on the load and bridge capacity only. If a relationship between analytical rating and subjective rating can be found. The estimation of bridges condition can be made only by determining the bridge analytical rating. Several attempts to correlate both methods using the conventional statistical analyses. As well as fuzzy logic have not been very succesful in providing the relationship between those rating methods. However, an attempt to utilize Artificial Neural Network (ANN) to correlate the analytical rating for railroad and bridge parameter with bridge subjective rating in Chicago has produced succesful results. This study describes the application of ANN in developing the correlation between load rating and subjective rating as well as bridge parameter for highway bridges. The subjective rating in this study is limited to deck rating only. The data provided by California Department of Transportation (CALTRAN) is utilized for training and testing session. The results obtained in the first part showed that additional variables are needed along with load rating variable to provide acceptable prediction performance. After several rounds of improvement process the best reults obtained exhibits 77% of the data used for testing are predicted within the acceptable range. Generally, this study showed that the ANN has a potential to be used to predict the subjective rating if the proper input variables are applied