Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images

Reduction of Uncertainties for Damage Identification of Bridge Based on Fuzzy Nearness and Modal Data

To avoid the false results of deterministic identification methods induced by uncertainties, a fuzzy nearness-based method is proposed for the damage identification of bridge. An improved index based on ratios of modal shape components is used as identification measurements. The knowledge base for damage identification is established through corresponding relationship between fuzzified measurements and damage severities. The damage condition of test samples can be assessed based on approaching principle through fuzzy nearness with rules in knowledge base. A numerical analysis on a multigirder bridge considering uncertainty is presented to demonstrate the effectiveness of the proposed method. The results indicate that the fuzzy nearness-based method can achieve an accurate identification with success rate up to 93.75%. Antinoise analysis and the ability for dealing with incomplete information reveal its robustness

Experimental Investigation of Damage Detection in Beam Using Dynamic Excitation System

Most structural failures are due to break in consisting materials. These breaks begin with a crack, the extension of which is a serious threat to the behaviour of structure. Thus the methods of distinguishing and showing cracks are the most important subjects being investigated. In this article, a new smart portable mechanical system to detect damage in beam structures via using fuzzy-genetic algorithm is introduced. Acceleration-time history of the three points of beam is obtained. The signals are then decomposed into smaller components using new EMD (Empirical Mode Decomposition) method with every IMF containing a specific range of frequency. The dominant frequencies of the structure are obtained from these IMFs using Short-term Fourier transform. Subsequently, a new method of damage detection in simply supported beams is introduced based on fuzzy-genetic algorithm. The new method is capable of identifying the location and intensity of the damage. This algorithm is developed to detect the location and intensity of the damage along the beam, which can detect the damage location and intensity based on the pattern of beam frequency variations between undamaged and damaged states

Structural health monitoring of beams with moving oscillator: theory and laboratory

U ovom se radu daje eksperimentalni i teoretski prikaz novog inteligentnog prenosivog mehaničkog sustava za otkrivanje oštećenja u kojem se koristi neizraziti genetski algoritam i metoda empirijskog rastavljanja (EMD). Za te je potrebe akcelerometrima izmjereno ubrzanje u vremenu na tri točke proste grede. Dobiveni signal rastavljen je metodom EMD na male komponente. Svaka komponenta sadrži određeni raspon frekvencija. Na kraju je provedeno projektiranje predloženog algoritma kako bi se utvrdilo mjesto i razina oštećenja na temelju obrasca variranja frekvencija na neoštećenoj i oštećenoj gredi.In this paper, a new intelligent portable mechanical system is introduced experimentally and theoretically to detect damage employing the fuzzy-genetic algorithm and EMD. For this purpose, the acceleration-time history is obtained from three points of a simply-supported beam utilizing accelerometer sensors. The gained signal is decomposed into small components by using an EMD method. Each decomposed component contains a specific frequency range. Finally, the proposed algorithm is designed to find the location and severity of damage through the frequency variation pattern among the safe and the damaged beam

Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner

Identification of Transverse Crack in a Cracked Cantilever Beam Using Fuzzy Logic and Kohonen Network

The issue of crack detection and diagnosis has gained wide spread industrial interest. Crack/damage affects the industrial economic growth. Generally damage in a structural element may occur due to normal operations, accidents, deterioration or severe natural events such as earth quake or storms. Damage can be analyzed through visual inspection or by the method of measuring frequency, mode shape and structural damping. Damage detection by visual inspection is a time consuming method and measuring of mode shape as well as structural deflection is difficult rather than measuring frequency. As Non- destructive method for the detection of crack is favorable as compared to destructive methods. So, our analysis has been made on the basis of non-destructive methods with the consideration of natural frequency. Here the crack is transverse surface crack. In the current analysis, methodologies have been developed for damage detection of a cracked cantilever beam using analytical, fuzzy logic, kohonen network as well as experimental. Theoretical analysis has been carried out to calculate the natural frequency with the consideration of mass and stiffness matrices. The data obtained from theoretical analysis has been fed to fuzzy controller as well as the kohonen competitive learning network. The Fuzzy Controller uses the different membership functions as input as well as output. The input parameters to the Fuzzy Controller are the first three natural frequencies. The output parameters of the fuzzy controller are the relative crack depth and relative crack location. Several Fuzzy rules have been trained to obtain the results for relative crack depth and relative crack location. Kohonen network is nothing but a competitive learning network is used here for the detection of crack depth and location. It is processed through a vector quantization algorithm. A comparative study has been made between fuzzy logic technique and Kohonen network technique after experimental verification. It has been observed that the process of kohonen network can predict the depth and location accurately as close to fuzzy logic technique

Vibration Analysis of Cracked Beam using Intelligent Technique

Structural systems in a wide range of Aeronautical, Mechanical and Civil Engineering fields are prone to damage and deterioration during their service life. So an effective and reliable damage assessment methodology will be a valuable tool in timely determination of damage and deterioration in structural members. Interest in various damage detection methods has considerably increased over the past two decades. During this time many detection methods founded on modal analysis techniques have been developed. Non-destructive inspection techniques are generally used to investigate the critical changes in the structural parameters so that an unexpected failure can be prevented. These methods concentrate on a part of the structure and in order to perform the inspection, the structure needs to be taken out of service. Since these damage identification techniques require a large amount of human intervention, they are passive and costly methods

Study of Computational and Experimental Methodologies for Cracks Recognition of Vibrating Systems using Modal Parameters

Mostly the structural members and machine elements are subjected to progressive static and dynamic loading and that may cause initiation of defects in the form of crack. The cause of damage may be due to the normal operation, accidents or severe natural calamities such as earthquake or storm. That may lead to catastrophic failure or collapse of the structures. Thereby the importance of identification of damage in the structures is not only for leading safe operation but also to prevent the loss of economy and lives. The condition monitoring of the engineering systems is attracted by the researchers and scientists very much to invent the automated fault diagnosis mechanism using the change in vibration response before and after damage. The structural steel is widely used in various engineering systems such as bridges, railway coaches, ships, automobiles, etc. The glass fiber reinforced epoxy layered composite material has become popular for constructing the various engineering structures due to its valuable characteristics such as higher stiffness and strength to weight ratio, better damage tolerance capacity and wear resistance. Therefore, layered composite and structural steel have been taken into account in the current study. The theoretical analysis has been performed to measure the vibration signatures (Natural Frequencies and Mode Shapes) of multiple cracked composite and structural steel. The presence of the crack in structures generates an additional flexibility. That is evaluated by strain energy release rate given by linear fracture mechanics. The additional flexibility alters the dynamic signatures of cracked beam. The local stiffness matrix has been calculated by the inverse of local dimensionless compliance matrix. The finite element analysis has been carried out to measure the vibration signatures of cracked cantilever beam using commercially available finite element software package ANSYS. It is observed from the current analysis, the various factors such as the orientation of cracks, number and position of the cracks affect the performance and effectiveness of damage detection techniques. The various automated artificial intelligent (AI) techniques such as fuzzy controller, neural network and hybrid AI techniques based multiple faults diagnosis systems are developed using vibration response of cracked cantilever beams. The experiments have been conducted to verify the performance and accuracy of proposed methods. A good agreement is observed between the results

Multiple Damage Identification of Beam Structure Using Vibration Analysis and Artificial Intelligence Techniques

This thesis investigates the problem of multiple damage detection in vibrating structural members using the dynamic response of the system. Changes in the loading patterns, weakening/degeneration of structures with time and influence of environment may cause cracks in the structure, especially in engineering structures which are developed for prolonged life. Hence, early detection of presence of damage can prevent the catastrophic failure of the structures by appropriately monitoring the response of the system. In recent times, condition monitoring of structural systems have attracted scientists and researchers to develop on line damage diagnostic tool. Primarily, the structural health monitoring technique utilizes the methodology for damage assessment using the monitored vibration parameters. In the current analysis, special attention has been focused on those methods capable of detecting multiple cracks present in system by comparing the information for damaged and undamaged state of the structure. In the current research, methodologies have been developed for damage detection of a cracked cantilever beam with multiple cracks using analytical, Finite Element Analysis (FEA), fuzzy logic, neural network, fuzzy neuro, MANFIS, Genetic Algorithm and hybrid techniques such as GA-fuzzy, GA-neural, GA-neuro- fuzzy. Analytical study has been performed on the cantilever beam with multiple cracks to obtain the vibration characteristics of the beam member by using the expressions of strain energy release rate and stress intensity factor. The presence of cracks in a structural member introduces local flexibility that affects its dynamic response. The local stiffness matrices have been measured using the inverse of local dimensionless compliance matrix for finding out the deviation in the vibrating signatures of the cracked cantilever beam from that of the intact beam. Finite Element Analysis has been carried out to derive the vibration indices of the cracked structure using the overall flexibility matrix, total flexibility matrix, flexibility matrix of the intact beam. From the research done here, it is concluded that the performance of the damage assessment methods depends on several factors for example, the number of cracks, the number of sensors used for acquiring the dynamic response, location and severity of damages. Different artificial intelligent model based on fuzzy logic, neural network, genetic algorithm, MANFIS and hybrid techniques have been designed using the computed vibration signatures for multiple crack diagnosis in cantilever beam structures with higher accuracy and considerably low computational time