A Model for Risk Cost and Bidding Price Prediction based on Risk Information in Plant Construction Projects

Kang, Hyun-Wook; Kim, Yong‐Su

doi:10.1007/s12205-018-0587-4

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…e price information collection includes the price information of construction projects, the cost information of decoration and repair projects, and the cost information of installation projects [23]. In interactive design, from the perspective of reducing information cognitive friction, designers need to take into account the actual operation routes of different user groups in different situations, which are reflected in the logo-oriented system.…”

Section: The Principle Of Bp Neural Network and Spea-ii Multiobjectiv...mentioning

confidence: 99%

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm

2022

Computational Intelligence and Neuroscience

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With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually relies on the traditional building performance simulation method to obtain the building performance index for optimization. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of building models and the configuration of computers, which leads to a long time for performance optimization, which cannot give efficient and accurate feedback to designers in engineering. Building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to put into practical projects. Therefore, this paper builds an accurate and efficient platform for building performance prediction and optimization to help designers make decisions combined with BP neural network and the SPEA-II multiobjective optimization algorithm. Besides, the optimization results of the case are quantitatively and qualitatively analyzed and presented in visual form based on the BP neural network prediction model. Quantitative analysis includes the evolution process of solution set, convergence process, and comprehensive quality evaluation of solution set. Qualitative analysis includes Pareto frontier and optimal architectural scheme analysis. Finally, the conclusion shows that the platform prediction and optimization can give accurate and reliable optimal solution, and the optimal building scheme is reasonable and has high engineering application value.

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Section: The Principle Of Bp Neural Network and Spea-ii Multiobjectiv...mentioning

confidence: 99%

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm

2022

Computational Intelligence and Neuroscience

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show abstract

“…The next study by Kang and Kim (2018) predicts the risk cost and the bidding price of a plant construction project based on the surveyed risk information. The model structure is designed and a prototype program is developed for the analysis.…”

Section: Project Performance Prediction Modelsmentioning

confidence: 99%

Neural network models for actual cost prediction in Greek public highway projects

Aretoulis

2019

IJPOM

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Selected public Greek highway projects are examined in order to produce models to predict their actual construction cost based on data available at the bidding stage. Twenty highway projects, constructed in Greece, with similar type of available data were examined. Considering each project's attributes and the actual cost, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most predictive project variables. Additionally, the WEKA application, through its attribute selection function, highlighted the most efficient subset of variables. These selected variables through correlation analysis and WEKA are used as input neurons for neural network models. FANN Tool is used to construct neural network models. The optimum neural network model produced a mean squared error with a value of 7.68544E-05 and was based on budgeted cost, lowest awarding bid, technical work cost and electromechanical work cost.

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Predict a model of the reliability engineering approach to estimating, risk management, and cost predicting for private residential projects in Iran

et al. 2022

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A Model for Risk Cost and Bidding Price Prediction based on Risk Information in Plant Construction Projects

Cited by 6 publications

References 8 publications

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm

Neural network models for actual cost prediction in Greek public highway projects

Predict a model of the reliability engineering approach to estimating, risk management, and cost predicting for private residential projects in Iran

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