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

Inspection and Monitoring of Structural Damage Using Vibration Signatures and Smart Techniques

The structural damage detection plays an important role in the evaluation of structural systems and to ensure their safety. Structures like large bridges should be continuously monitored for detection of damage. The cracks usually change the physical parameters like stiffness and flexibility which in turn changes the dynamic properties such as natural frequencies and mode shapes. Crack detection of a beam element comprises of two aspects: the first one is the forward problem which is achieved from the Eigen parameters and the second one is the process to locate and quantify the effect of damage and is termed as ‘inverse process of damage detection’. In the present investigation the analytical and numerical methods are known as the forward problem includes determination of natural frequencies from the knowledge of beam geometry and crack dimension. The vibration signals are derived from the forward problem is exploited in the inverse problem. The natural frequency changes occur due to the various reasons such as boundary condition changes, temperature variations etc. Among all the changes boundary condition changes are the most important factors in structural elements. Many major structures like bridges are made up of uniform beams of unknown boundary conditions. So in the present investigation two of the boundary conditions i.e. fixed -free and fixed- fixed are considered. Using the forward solution method, the natural frequencies are determined. In the inverse solution method various Artificial Intelligence (AI) techniques with their hybrid methods are proposed and implemented. Damage detection problems using Artificial Intelligence techniques require a number of training data sets that represent the uncracked and cracked scenarios of practical structural elements. In the second part of the work different AI techniques like Fuzzy Logic, Genetic Algorithm, Clonal Selection Algorithm, Differential Evolution Algorithm and their hybrid methods are designed and developed. In summary this investigation is a step towards to forecast the position of the damage using the Artificial Intelligence techniques and compare their results. Finally, the results from the Artificial Intelligence techniques and their hybridized algorithms are validated by doing experimental analysis

Online Function Monitoring of Faulty Beam

Early detection of damage is of special concern for engineering structures. The traditional methods of damage detection include visual inspection or instrumental evaluation. A comparatively recent development for the diagnosis of structural crack location and size by using the finite element method and Fuzzy logics techniques. A method based on measurement of natural frequencies is presented for detection of the location and size of a crack in a cantilever beam. Numerical and programming in MATLAB is done for solving the eular equation for uncrack beam to obtain first three natural frequencies of different modes of vibration considering various boundary conditions for the beam. Here ALGOR software package is used for finite element analysis of both crack and un-crack cantilever beam taking input file as a CAD design developed in AUTOCAD 2006. Experiments is done for total 21 models of crack beam having different crack location and crack depth and it generates natural frequency for 5 modes of vibration. Fuzzy controller here used comprises of three input variables (fnf, snf, tnf) with Gaussian MF and two output variables (rcl, rcd) are generated with Triangular MF. Fuzzy analysis is done based on some set of fuzzy rules obtained from the information supplemented by finite element analysis and numerical analysis. The proposed approach has been verified by comparing results obtained from fuzzy logic technique and finite element analysis. The results show the successful detection of the undamaged and damaged states of structural part with very good accuracy and repeatability

Diagnosis of Damages in Beam Structures using Vibration Parameters and Artificial Intelligence Techniques

In the present analysis, special attention has been focused for detecting the damages present in Al, composite and steel beam structures by comparing the characteristics of damaged and undamaged state of the structures. In the current research, damage detection of damaged cantilever and fixed-fixed beam is carried out using numerical, Finite element analysis (FEA), fuzzy logic and neural network techniques. Numerical analysis has been performed on the cantilever beam & fixed-fixed beam with damage in the transverse direction to obtain the vibration parameters of the beam members utilizing the expression of strain energy release rate and stress intensity factor. The presence of damage in a structural member introduces local stiffness that affects its dynamic characteristics. The local stiffness matrices have been determined using the inverse of local dimensionless compliance matrix for finding out the deviations in the vibrating signatures of the damaged beam structures from that of the intact beams. Finite Element Analysis has been carried out to derive the vibration indices of the damaged structures using the overall stiffness matrix, total stiffness matrix, stiffness matrix of the intact beams. It is concluded from the conducted research that the performance of the damage diagnosis techniques depends on several factors for example, the material type, the number of sensors used for acquiring the dynamic response, position and severity of damages. Different artificial intelligent model based on fuzzy logic, neural network have been designed using the estimated vibration signatures for damage diagnosis in beam structures with higher precision and remarkably low calculating time

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

Intelligent Diagnosis and Smart Detection of Crack in a Structure from its Vibration Signatures

In recent years, there has been a growing interest in the development of structural health monitoring for vibrating structures, especially crack detection methodologies and on-line diagnostic techniques. In the current research, methodologies have been developed for damage detection of a cracked cantilever beam using analytical, fuzzy logic, neural network and fuzzy neuro techniques. The presence of a crack in a structural member introduces a local flexibility that affects its dynamic response. For finding out the deviation in the vibrating signatures of the cracked cantilever beam the local stiffness matrices are taken into account. Theoretical analyses have been carried out to calculate the natural frequencies and mode shapes of the cracked cantilever beam using local stiffness matrices. Strain energy release rate has been used for calculating the local stiffness of the beam. The fuzzy inference system has been designed using the first three relative natural frequencies and mode shapes as input parameters. The output from the fuzzy controller is relative crack location and relative crack depth. Several fuzzy rules have been developed using the vibration signatures of the cantilever beam. A Neural Network technique using multi layered back propagation algorithm has been developed for damage assessment using the first three relative natural frequencies and mode shapes as input parameters and relative crack location and relative crack depth as output parameters. Several training patterns are derived for designing the Neural Network. A hybrid fuzzy-neuro intelligent system has been formulated for fault identification. The fuzzy controller is designed with six input parameters and two output parameters. The input parameters to the fuzzy system are relative deviation of first three natural frequencies and first three mode shapes. The output parameters of the fuzzy system are initial relative crack depth and initial relative crack location. The input parameters to the neural controller are relative deviation of first three natural frequencies and first three mode shapes along with the interim outputs of fuzzy controller. The output parameters of the fuzzy-neuro system are final relative crack depth and final relative crack location. A series of fuzzy rules and training patterns are derived for the fuzzy and neural system respectively to predict the final crack location and final crack depth.To diagnose the crack in the vibrating structure multiple adaptive neuro-fuzzy inference system (MANFIS) methodology has been applied. The final outputs of the MANFIS are relative crack depth and relative crack location. Several hundred fuzzy rules and neural network training patterns are derived using natural frequencies, mode shapes, crack depths and crack locations. The proposed research work aims to broaden the development in the area of fault detection of dynamically vibrating structures. This research also addresses the accuracy for detection of crack location and depth with considerably low computational time. The objective of the research is related to design of an intelligent controller for prediction of damage location and severity in a uniform cracked cantilever beam using AI techniques (i.e. Fuzzy, neural, adaptive neuro-fuzzy and Manfis)

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

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

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

Dynamic Analysis of Cracked Rotor in Viscous Medium and its Crack Diagnosis Using Intelligent Techniques

Fatigue cracks have high potential to cause catastrophic failures in the rotor which can lead to catastrophic failure if undetected properly and in time. This fault may interrupt smooth, effective and efficient operation and performance of the machines. Thereby the importance of identification of crack in the rotor 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 dynamic response before and after damage. When the rotor with transverse crack immersed in the viscous fluid, analysis of cracked rotor is difficult and complex. The analysis of cracked rotor partially submerged in the viscous fluid is widely used in various engineering systems such as long spinning shaft used drilling the seabed for the extracting the oil, high-speed turbine rotors, and analysis of centrifuges in a fluid medium. Therefore, dynamic analysis of cracked rotor partially submerged in the viscous medium have been presented in the current study. The theoretical analysis has been performed to measure the vibration signatures (Natural Frequencies and Amplitude) of multiple cracked mild steel rotor partially submerged in the viscous medium. The presence of the crack in rotor generates an additional flexibility. That is evaluated by strain energy release rate given by linear fracture mechanics. The additional flexibility alters the dynamic characteristics of cracked rotor in a viscous fluid. 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 characteristics of cracked rotor partially submerged in the viscous medium using commercially available finite element software package ANSYS. It is observed from the current analysis, the various factors such as the viscosity of fluid, depth and position of the cracks affect the performance of the rotor and effectiveness of crack detection techniques. Various Artificial Intelligent (AI) techniques such as fuzzy logic, hybrid BPNN-RBFNN neural network, MANFIS and hybrid fuzzy-rule base controller based multiple faults diagnosis systems are developed using the dynamic response of rotating cracked rotor in a viscous medium to monitor the presence of crack. Experiments have been conducted to authenticate the performance and accuracy of proposed methods. Good agreement is observed between the results