摘要:

Abstract:Traditional methods for structural monitoring and damage assessment have been implemented largely through visual inspection and on‐site tests. A system for automating this process should be able to record the various signatures of the structure to be monitored and issue a warning signal if there is a damage‐related change in those signatures. In this paper, a general system for structural damage monitoring is proposed based on observations of other researchers and the results obtained from a case study of a physical and analytical model of a five‐story steel frame. The proposed diagnostic system utilizes neural networks for identifying the damage associated with changes in structural signatures. The system is independent of the type of signatures used for monitoring. Two sets of neural networks were developed. The first set was trained with the results of a series of shaking‐table experiments, while the second set was trained with the output produced from a finite‐element model of the same test structure. The results show that the proposed system provides a suitable framework for automatic structural m

ISSN:1093-9687    

DOI:10.1111/j.1467-8667.1994.tb00364.x

出版商:Blackwell Publishing Ltd    

年代:1994

数据来源: WILEY

摘要:

Abstract:The pattern‐mapping, pattern‐classification, and optimization capabilities of neural networks have been used to solve a number of structural analysis and design problems. Most applications exploit the pattern‐mapping capability and are based on the back‐propagation paradigm for neural networks. There are a number of factors that influence the performance of these networks. This paper initially discusses these factors and the domain‐dependent and ‐independent techniques presently available for improving performance. The paper then considers the effect of representation, selected for the input/output pattern pairs, on the performance of these networks and demonstrates that representations based on dimensionless terms, derived from dimensional analysis, lead to improved performance. It is shown that dimensional analysis provides a representational framework, with reduced dimensionality and embedded domain knowledge, within which effective learning can take place and that this representational change can be used to enhance the domain‐independent and ‐dependent techniques presently available for improving performance of

ISSN:1093-9687    

DOI:10.1111/j.1467-8667.1994.tb00365.x

出版商:Blackwell Publishing Ltd    

年代:1994

数据来源: WILEY

摘要:

Abstract:This paper describes StructNet, a computer application developed to select the most effective structural member materials given a building project's attributes. The system analyzes 15 parameters of a building project (e.g., available site space, budget, height) and determines the most appropriate structural system for the beam, column, and slab structural members. This paper first describes the process for selecting a structural system for a building. It was very important to understand this process before determining the best type and structure for the computer application. Then a comparison between a neural network approach and a rule‐based expert‐system approach for this application is presented. A discussion of the reasons for selecting a neural network approach is given. The StructNet application is described in detail, including the testing of the network. Along with the testing of the network is a discussion of how varying the learning rate and error limit affect the performance of the neural network application. The testing of the network shows that the program can reasonably select the same structural system types as the expert used to collect the training project data. Since the system will be used only as a preliminary tool to limit the number of possible structural systems for a project, the accuracy of the system is acceptable. However, additional experimentation needs to be conducted to determine the accuracy and practical use of this application. The final sections of the paper discuss the lack of adequate testing procedures for neural networks used in applications for unstructured or ill‐defined decision making. The use of these types of networks and their relevance to the civil engineering computer field are also disc

ISSN:1093-9687    

DOI:10.1111/j.1467-8667.1994.tb00366.x

出版商:Blackwell Publishing Ltd    

年代:1994

数据来源: WILEY

摘要:

Abstract:A new approach is explored for assessing the safety condition of civil engineering structures following strong‐motion earthquakes that utilizes artificial intelligence to collectively consider several factors in the assessment process. Following this approach, various types of information related to the damage condition of the structure are input into a neural network computer program that has been trained to generate safety assessments. The network acts on the input information according to weighting factors established from data on past seismic events to generate a condition assessment. This study focused on utilizing various quantitative damage indices in formulating a safety assessment. Development of a neural network for this purpose was accomplished using response data from structural systems tested in the laboratory. The neural network generated more reliable assessments than could be obtained using any single indicator or from a linear regression model that utilized all indicators. The network was used successfully to assess the condition of a real structure damaged in an earthquake. A network of this kind may be useful in automatic and immediate condition assessment following a seismic event and as an aid to an expert in determining such assessment

ISSN:1093-9687    

DOI:10.1111/j.1467-8667.1994.tb00367.x

出版商:Blackwell Publishing Ltd    

年代:1994

数据来源: WILEY

摘要:

Abstract:A neural network–based decision support system has been designed and simulated to be used as a filter to improve the system performance of large incomplete databases enhanced with maybe algebra. To train the network, a knowledge‐acquisition module equipped with a fuzzy logic technique was used to automatically generate a set of training pairs according to the semantics of the underlying database, the specific characteristics of the user query, and user requirements. Based on the notion of relative graded membership, a fuzzy logic–based controller was used to monitor and measure the quality of each training pattern as a means to generate a set of “good” training pairs. Finally, the proposed scheme has been simulated and analyzed to determine the effectiveness of the automatic training pairs generatio

ISSN:1093-9687    

DOI:10.1111/j.1467-8667.1994.tb00368.x

出版商:Blackwell Publishing Ltd    

年代:1994

数据来源: WILEY

摘要:

Abstract:In the past few years, neural networks have emerged as a problem‐solving technique with capabilities suited to many civil engineering problems. Among the various neural network paradigms available, back‐propagation is by far the most utilized for its relatively simple mathematical proofs and good generalization capabilities. Despite its capabilities, back‐propagation suffers from several problems that hinder the development of practical neural network applications. These include slow training, ill‐defined knowledge representation and problem structuring, and nonguided design of an optimal network configuration for adequate generalization. This paper represents an effort to guide the process of developing practical neural network applications using back‐propagation. The paper starts with a brief description of back‐propagation mathematics. Some of the heuristics and techniques used to overcome back‐propagation problems, particularly lack of generalization, are identified and outlined, along with areas of potential improvements to the paradigm. An application development methodology is proposed utilizing the identified heuristics and techniques. The methodology provides a structured framework for designing and implementing practical neural network applications wit

ISSN:1093-9687    

DOI:10.1111/j.1467-8667.1994.tb00369.x

出版商:Blackwell Publishing Ltd    

年代:1994

数据来源: WILEY