Neural network ensemble-based parameter sensitivity analysis in civil engineering systems

Cao, Maosen; Pan, Lixia; Gao, Yong; Novák, Drahomı́r; Ding, Z. C.; Lehký, David; Li, X. L.

doi:10.1007/s00521-015-2132-4

Cited by 63 publications

(38 citation statements)

References 36 publications

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“…e application of BPNN has been more common in the research, but the use of BPNN to explore the competitiveness of enterprises is still rare. BPNN are often used in performance evaluation [34], pattern recognition [35], and simulation [36]. Zhiyuan et al [37] studied the enterprise competitiveness evaluation based on the wavelet neural network forecasting system.…”

Section: 2mentioning

confidence: 99%

A Grey‐BPNN Model for Evaluating the Competitiveness of Chinese Contractors in the High‐Speed Rail Market in Europe

Lin

Shi

Chen

et al. 2019

Advances in Civil Engineering

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With the planning and progress of the construction of the trans-Eurasian high-speed rail (HSR) network, it becomes an important issue for Chinese contractors to enter the European HSR market. Facing the world's most competitive contractors and their high technology levels, Chinese contractors will need to know their advantages and disadvantages, so as to make necessary improvements. In this study, contractors for HSR are divided into two groups: construction contractors and rail equipment suppliers. In order to evaluate the competitiveness of HSR contractors, a Grey-BPNN model that combines the grey relational analysis and backpropagation neural network (BPNN) is proposed. e Grey-BPNN model is expected to analyze the overall competitiveness of Chinese contractors in the European HSR market and provide informative decision support for them. e study results show the following: (1) in the field of HSR construction, the competitiveness gap between the top-tier Chinese contractors and the most competitive international contractors is small. Chinese contractors' competitive advantages lie in medium-and low-technology-level projects, with a strong development potential. However, they highly depend on Chinese domestic market and lack in intangible resources, like management ability and market development ability; (2) for rail equipment suppliers, China Railway Rolling Stock Corporation (CRRC) ranks among the top-tier leaders of the international market. CRRC's greatest competitor in the European HSR market is Siemens, and CRRC is much more competitive than others in the sustainable development capability. However, CRRC needs to increase the quantity of patents and Research and Development (R&D) expenditures in transportation. As a weak transportation patent holder, CRRC has a potential risk of getting intellectual property litigations in the European HSR market.

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Section: 2mentioning

confidence: 99%

A Grey‐BPNN Model for Evaluating the Competitiveness of Chinese Contractors in the High‐Speed Rail Market in Europe

Lin

Shi

Chen

et al. 2019

Advances in Civil Engineering

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“…Recent studies have reported that ANNs can implement some component analysis methods such as sensitivity analysis [49]. Likewise, they can be used to conduct NLPCA.…”

Section: Nonlinear Principal Component Analysismentioning

confidence: 99%

“…The number of neurons at the input layer or output layer (M) fits the dimensions of the WPNEs. According to [49], the number of neurons of either the mapping layer or the remapping layer N should be chosen such that:…”

Section: Damage Feature Extractionmentioning

confidence: 99%

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

et al. 2017

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A sophisticated hierarchical neural network model for intelligent assessment of structural damage is constructed by the synergetic action of auto-associative neural networks (AANNs) and Levenberg-Marquardt neural networks (LMNNs). With the model, AANNs aided by the wavelet packet transform are firstly employed to extract damage features from measured dynamic responses and LMNNs are then utilized to undertake damage pattern recognition. The synergetic functions endow the model with a unique mechanism of intelligent damage identification in structures. The model is applied for the identification of damage in a three-span continuous bridge, with particular emphasis on noise interference. The results show that the AANNs can produce a low-dimensional space of damage features, from which LMNNs can recognize both the location and the severity of structural damage with great accuracy and strong robustness against noise. The proposed model holds promise for developing viable intelligent damage identification technology for actual engineering structures.

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“…Representative studies of the use of ANNs to analyze the sensitivity of parameters of an engineering system are e.g. [9][10][11][12]. The paper shows alternative SA techniques and theirs utilization for practical engineering example.…”

Section: Introductionmentioning

confidence: 99%

Methods of Sensitivity Analyses and theirs Role in Engineering

Drahom¡r¹,

Lehký²,

Cao³

et al. 2018

Proceedings of the International Symposium on Mechanical Engineering and Material Science (ISMEMS 2017)

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Abstract-The paper describes selected efficient methods for sensitivity analysis of engineering problems: The non-parametric rank-order statistical correlation between the basic random variables and the structural response variable, neural network ensemble-based sensitivity analysis and sensitivity analysis in terms of coefficient of variation. All three methods were utilized and compared for practical engineering example.

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Neural network ensemble-based parameter sensitivity analysis in civil engineering systems

Cited by 63 publications

References 36 publications

A Grey‐BPNN Model for Evaluating the Competitiveness of Chinese Contractors in the High‐Speed Rail Market in Europe

A Grey‐BPNN Model for Evaluating the Competitiveness of Chinese Contractors in the High‐Speed Rail Market in Europe

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

Methods of Sensitivity Analyses and theirs Role in Engineering

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