Ayaho Miyamoto

BRIDGE MANAGEMENT SYSTEM AND MAINTENANCE OPTIMIZATION FOR EXISTING BRIDGES

Of late, the maintenance of bridges has become a major social concern, and thus the development of a practical bridge management system (BMS) is required. This paper describes a study that attempts to develop a new BMS for deteriorated concrete bridges by evaluating the output results from a bridge rating expert system currently under development. The proposed BMS offers various cost minimization and quality maximization. Genetic algorithms (GAs) are adopted for solving the optimization problem. These algorithms, which are based on the theory of evolution, create a suitable individual (optimal) solution through the repetition of 3 operators: selection, crossover, and mutation. Furthermore, applications to several existing concrete bridges are presented so as to demonstrate the validity of the proposed BMS.

A Comparative Study of Modal Parameter Identification Based on Wavelet and Hilbert–Huang Transforms

Modal parameter identification is an important topic in vibration-based structural health monitoring. This paper presents a comparative study of the modal parameter identification of structures based on the continuous wavelet transform (WT) using the modified complex Morlet wavelet function and the improved Hilbert–Huang transform (HHT). Special attention is given to some implementation issues, such as the modal separation and end effect in the WT, the optimal parameter selection of the wavelet function, the new stopping criterion for the empirical mode decomposition and the end effect in the HHT. The capabilities of these two techniques are compared and assessed by using three examples: a numerical simulation for a damped system with two very close modes; an impact test on an experimental model with three well-separated modes; and an ambient vibration test on the Z24-bridge benchmark problem. The results demonstrate that both methods are applicable for the system with well-separated modes when the time-frequency resolutions are sufficiently taken into account. For the system with very close modes, the WT method seems to be more effective than HHT. One reason is that the frequency separation of HHT is partially dependent on the decomposition performance of the preprocess tool. Therefore, if the adjacent frequency components are very close, it is difficult to design appropriate parameters for the filters to separate them clearly.

Condition state evaluation of existing reinforced concrete bridges using neuro-fuzzy hybrid system

This article presents a new approach for developing a concrete bridge rating expert system for deteriorated concrete bridges, constructed from multi-layer neural networks. The system evaluates the performance of concrete bridges on the basis of a simple visual inspection and technical specifications. The main reason of applying the neural network is that it performs fuzzy inference in the network, facilitates refinement of the knowledge base by use of the back-propagation method, and prevents not only the inference mechanism of the expert system but also the knowledge base after machine learning from becoming a black box.

Development of a bridge management system for existing bridges

Abstract Recently, the rehabilitation of bridges has become a major social concern because the number of damaged bridges has increased in Japan. Thus, there is a need to develop a practical bridge management system. The present study is an attempt to develop a new bridge management system (J-BMS) for damaged concrete bridges. The J-BMS not only evaluates the performance of bridges, but also offers a rehabilitation strategy based on a combination of maintenance cost minimization and quality maximization. Furthermore, application to existing concrete bridges is presented so as to demonstrate the validity of the system.

The moment segmentation analysis of heart sound pattern

UNLABELLED This paper presents two new ideas. The first one is to apply the Viola integral waveform method to analyze the heart sounds recorded by an electric stethoscope, and the multi-scale moment analysis is proposed to locate each cycle of heart sounds. A fast algorithm for calculating characteristic waveform (CW) and characteristic moment waveform (CMW) of heart sound can be expressed by the Viola integral method, and their calculation time has nothing to do with their scales. The second idea is easier to segment the heart sound based on its approximate cyclical characteristic than the ordinary methods. Each heart sound cycle can be quickly found by CMWs Local Extreme Points (LEPs). Based on the information of LEPs and CW, a high accurate search algorithm to segment S1 and S2 sounds is submitted. By numerical experiments, the important parameters of time scale delta=0.05s for CW and l=0.45s for CMW are obtained and validated for segmentation of heart sound. CONCLUSION More exact segmentation boundaries of the heart sound signal could be located fast in an automated way, and a further performance analysis is presented. Owing to the use of the rhythm of CMW curves, the proposed method not only gives a higher success segmentation rate, but also it is actually simpler and faster than the wavelet method.

Performance evaluation of concrete slabs of existing bridges using neural networks

This paper presents a novel approach for developing a performance evaluation system for concrete slabs of existing bridges. The system evaluates the performance of concrete slabs under deterioration on the basis of expert knowledge. Characteristic features of this study are the definition of bridge performance, the performance evaluation system, and the use of neural networks. The proposed approach performs inference in the network, facilitates refinement of the knowledge base embedded in the system by the back propagation method, and prevents not only the inference mechanism of the system but also knowledge base after machine learning from becoming a black box. The numerical examples and conclusions reveal that the proposed approach demonstrates real potential for practical applications.

NONLINEAR DYNAMIC ANALYSIS OF REINFORCED CONCRETE SLABS UNDER IMPULSIVE LOADS

A triaxial failure criterion is applied together with the theory of plasticity for modeling concrete in reinforced concrete (RC) slabs subjected to impulsive loads. The transverse shear stresses are extrapolated and then included into the dynamic layered finite element procedure, as it is assumed to affect not only the ultimate behaviors but also the failure modes. A provision for material nonlinearity, cracking in concrete elements, and the loading and unloading phenomena are adopted in this study. Verification of the analytical procedure is carried out by means of comparisons with test rsults on full-scale RC slabs. It is found that ultimate behaviors, the post-failure behaviors as well as the failure modes, can be predicted accurately using the proposed procedure. The procedure could serve as a tool for a dynamic design method for concrete slab structures subjected to impulsive loads.

ANALYSIS OF FAILURE MODES FOR REINFORCED CONCRETE SLABS UNDER IMPULSIVE LOADS

The faillure modes that can be associated with soft impulsive loads for reinforced concrete slabs are analytically predicted using a nonlinear dynamic layered finite element method. The failure mechanism at the ultimate states is also predicted and found to be in good agreement with the actual phenomenon. The effects of loading rates during impulsive loadings are also considered, and it is found that the failure modes are affected by the loading rates and also impulse from an impulsive load function. The failure modes can be predicted using the proposed analytical method. The failure modes can be classified into three different regions based on the loading rates. It can be concluded that this method may be used in future as a tool for a dynamic design method for reinforced concrete structure under soft impulsive loads.

Practical Applications of a Bridge Management System in Japan

Recently, the necessity of developing a practical bridge management system (BMS) has been pointed out in Japan, because the maintenance of existing bridges has become a major social concern. The aim of this study was to develop a practical BMS for deteriorated concrete bridges. The proposed system (J-BMS) uses multilayered neural networks to predict deterioration processes in existing bridges, to construct an optimal maintenance plan for repair or strengthening measures based on minimizing life-cycle cost, and to estimate the maintenance cost. A comparison of the results of applying this system to some actual in-service bridges with the results of questionnaire surveys of experts indicates that optimal maintenance planning as well as bridge rating can be predicted accurately by this system.

Imaging-Based Rating for Corrosion States of Weathering Steel Using Wavelet Transform and PSO-SVM Techniques

AbstractWeathering steel with a natural corrosion-resistant feature has been widely applied to the structural components of steel bridges. However, severe surface corrosion damage has been frequently observed in the weathering steels of bridges, which causes the performance degradation of the structure. Conventional visual classification approaches are time-consuming and subjective and cannot provide quantitative evaluation effectively and efficiently. This paper presents a new imaging-based intelligent method for quantitatively rating the corrosion states of weathering steel bridges. Images are characterized by image texture analysis using two-dimensional wavelet decomposition, from which both the local and global energy distributions of each detail subimage are extracted as representative features. To enhance the performance of a support vector machine (SVM) in corrosion state classification, a particle swarm optimization algorithm (PSO) is developed to obtain the optimal parameters of the SVM. A compar...