Assessment of Waterlogging Risk in the Deep Foundation Pit Projects Based on Projection Pursuit Model

Wu, Han; Wang, Junwu

doi:10.1155/2020/2569531

Cited by 10 publications

(7 citation statements)

References 45 publications

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“…The proportion of old workers (R 15 ) is a measure of the population structure of workers on the construction site. Generally speaking, the older workers are, the more easily they get hurt in safety accidents [27]. Therefore, the lower the value of R 15 , the lower the construction safety risk.…”

Section: Acquisition Methods Of the Index Datamentioning

confidence: 99%

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

Zhang

Wang

et al. 2022

Applied Sciences

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Subway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing performance of the early risk warning system in subway station construction, a novel model based on least-squares support vector machines (LSSVM) optimized by quantum-behaved particle swarm optimization (QPSO) was proposed. First, early warning factors from five aspects (man, machine, management, material, and the environment) were selected based on accident causation theory and literature research. The data acquisition method of each risk factor was provided in detail. Then, the LSSVM with strong small sample analysis and nonlinear analysis abilities was chosen to give the early warning. To further ameliorate the early warning accuracy of the LSSVM, QPSO with a strong global retrieval ability was used to find the optimal calculation parameters of the LSSVM. Seventeen subway stations of Chengdu Metro Line 11 in China were picked as the empirical objects. The results demonstrated that the best regularization parameter was 1.742, and the best width parameter was 14.167. The number of misjudged samples of the proposed model was 1, and the early warning error rate was only 4.41%, which met the needs of engineering practice. Compared with the classic and latest methods, the proposed model was found to have a faster prediction speed and higher prediction accuracy.

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Section: Acquisition Methods Of the Index Datamentioning

confidence: 99%

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

Zhang

Wang

et al. 2022

Applied Sciences

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“…The larger the catchment area of a low-lying subway station project, the more prone it is to waterlogging disaster, and the greater the loss rate of loss disaster. (3) When there are geomorphic elements, such as ponds, high slopes, irrigation canals and rivers, around the subway station under construction, the possibility of waterlogging disasters increases sharply [3]. (4) The height of the retaining wall directly determines whether waterlogging water enters the subway station project.…”

Section: The Analysis Of Input Variablesmentioning

confidence: 99%

“…After sudden flood disasters, the economic losses of subway stations and other construction projects are extremely complicated. According to the causes of disaster losses, disaster losses are related not only to the characteristics of flood disasters, but also to the engineering and management characteristics of subway station projects [3]. From the perspective of accounting and finance, disaster losses are related to the disaster loss rate and the value of assets.…”

Section: Introductionmentioning

confidence: 99%

A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project

Wang

2021

Mathematics

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In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm (SSA), the least-squares support vector machine (LSSVM) and the mean impact value (MIV) method. First, in this study, 11 input variables are determined from the disaster loss rate and asset value, and a complete method is provided for acquiring and processing data of all variables. Then, the SSA method, with strong optimization ability, fast convergence and few parameters, is used to optimize the kernel function and the penalty factor parameters of the LSSVM. Finally, the MIV is used to identify the important input variables, so as to reduce the predicted input variables and achieve higher calculation accuracy. In addition, 45 station projects in China were selected for empirical analysis. The empirical results revealed that the linear correlation between the 11 input variables and output variables was weak, which demonstrated the necessity of adopting nonlinear analysis methods such as the LSSVM. Compared with other forecasting methods, such as the multiple regression analysis, the backpropagation neural network (BPNN), the BPNN optimized by the particle swarm optimization, the BPNN optimized by the SSA, the LSSVM, the LSSVM optimized by the genetic algorithm, the PSO-LSSVM and the LSSVM optimized by the Grey Wolf Optimizer, the model proposed in this paper had higher accuracy and stability and was effectively used for forecasting economic loss in subway station engineering projects due to rainstorms.

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“…Pit Project Construction Costs. To prevent the output of the self-coding network from reaching saturation or even prematurely falling into the local minimum due to the large differences in the absolute values of the input data, it is necessary to normalize the input and output vectors of the training data sample set X in advance [43].…”

Section: Normalization Of the Influencing Factors Of Foundationmentioning

confidence: 99%

Study on Foundation Pit Construction Cost Prediction Based on the Stacked Denoising Autoencoder

Liu

Liu²,

et al. 2020

Mathematical Problems in Engineering

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To accurately predict the construction costs of foundation pit projects, a model based on the stacked denoising autoencoder (SDAE) is constructed in this work. The influencing factors of foundation pit project construction costs are identified from the four attributes of construction cost management, namely, engineering, the environment, the market, and management. Combined with Chinese national standards and the practice of foundation pit project management, a method of the quantization of the influencing factors is presented. 60 deep foundation pit projects in China are selected to obtain 13 main characteristic factors affecting these project construction cost by using the rough set. Then, considering the advantages of the SDAE in dealing with complex nonlinear problems, a prediction model of foundation pit project construction costs is created. Finally, this paper employs these 60 projects for a case analysis. The case study demonstrates that, compared with the actual construction costs, the calculation error of the proposed method is less than 3%, and the average error is only 1.54%. In addition, three error analysis tools commonly used in machine learning (the determination coefficient, root mean square error, and mean absolute error) emphasize that the calculation accuracy of the proposed method is notably higher than those of other methods (Chinese national code, the multivariate return method, the BP algorithm, the BP model optimized by the genetic algorithm, the support vector machine, and the RBF model). The relevant research results of this paper provide a useful reference for the prediction of the construction costs of foundation pit projects.

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Assessment of Waterlogging Risk in the Deep Foundation Pit Projects Based on Projection Pursuit Model

Cited by 10 publications

References 45 publications

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project

Study on Foundation Pit Construction Cost Prediction Based on the Stacked Denoising Autoencoder

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