Developing an SVM based risk hedging prediction model for construction material suppliers

Chen, Jieh Haur; Lin, Jia

doi:10.1016/j.autcon.2010.02.014

Cited by 41 publications

(13 citation statements)

References 30 publications

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“…With the wide convergence domain, the RBF has the advantage of being able to approximate an arbitrary non-linear and high-dimensional computation function, so it is the most widely used kernel function. Besides, RBF is a prior selection, since it effectively reduces complexity for inputs by only adjusting c and g [71].…”

Section: Parameter Optimization Of Svm Model Based On Particle Swarm mentioning

confidence: 99%

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Liu

Xie

Bian

et al. 2020

IJERPH

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Incorporating safety risk into the design process is one of the most effective design sciences to enhance the safety of metro station construction. In such a case, the concept of Design for Safety (DFS) has attracted much attention. However, most of the current research overlooks the risk-prediction process in the application of DFS. Therefore, this paper proposes a hybrid risk-prediction framework to enhance the effectiveness of DFS in practice. Firstly, 12 influencing factors related to the safety risk of metro construction are identified by adopting the literature review method and code of construction safety management analysis. Then, a structured interview is used to collect safety risk cases of metro construction projects. Next, a developed support vector machine (SVM) model based on particle swarm optimization (PSO) is presented to predict the safety risk in metro construction, in which the multi-class SVM prediction model with an improved binary tree is designed. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. Finally, the proposed framework is applied to a case study of metro station construction. The prediction results show the PSO–SVM model is applicable and reasonable for safety risk prediction. This research also identifies the most important influencing factors to reduce the safety risk of metro station construction, which provides a guideline for the safety risk prediction of metro construction for design process.

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Section: Parameter Optimization Of Svm Model Based On Particle Swarm mentioning

confidence: 99%

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Liu

Xie

Bian

et al. 2020

IJERPH

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“…SVM is an effective supervised machine learning algorithm for nonlinear regression analysis [23]. It has been successfully applied to predict natural gas emissions [24], protein subcellular localizations [25], and hedge financial risks for construction material suppliers [26].…”

Section: B Modeling Methodsmentioning

confidence: 99%

Optimization of Chemical Fungicide Combinations Targeting the Maize Fungal Pathogen, <italic>Bipolaris maydis</italic>: A Systematic Quantitative Approach

Wang

et al. 2015

IEEE Trans. Biomed. Eng.

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To control the southern corn leaf blight, a severe disease of maize around the world, a combination of fungicides is often more potent than using individual fungicides. However, the number of possible combinations increases exponentially with the increase of the number of fungicides combined and their concentrations. It is thus extremely challenging to identify effective fungicide combinations by trial and error from all possible combinations. In this paper, a systematic approach based on a support vector machine, a machine learning algorithm, is proposed to searching for the optimal combinations using only a limited number of measurements. The constructed model also incorporates information related to the inhibition rate (IR) and the cost of each composing fungicide into the optimization process. With this method, we show that only around 130 measurements on a coarse grid of concentrations out of thousands of possible experiments are sufficient to reconstruct the response model and to obtain the optimal fungicide combinations. Experimental results demonstrate that the optimized combinations can achieve an IR greater than 90%, while the required concentrations and the cost of individual fungicides are dramatically reduced. We anticipate that this method should be equally effective in the search for optimal combinations of multiple compounds in other diseases.

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“…SVM can be applied for either classification or regression problems. Some SVM implementations in construction management, introduced in works published in recent years, are the automated document classification for improving information flow in construction management systems [22], methodology of legal decision support aiming at mitigation of negative impacts of conflicts that occur in the course of construction projects [23], risk hedging prediction for construction material suppliers [24], modeling construction contractors default prediction [25], prediction of company failure in the construction industry [26], and dynamical prediction of construction project success [27].…”

Section: Literature Reviewmentioning

confidence: 99%

On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

Juszczyk

2019

Buildings

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The completion of a bridge construction project within budget is one of the project’s key factors of success. This prerequisite is more likely to be achieved if the cost estimates, especially those provided in the early stage of a project, are realistic and close to the actual costs. The paper presents the research results on the development of a cost prediction model based on machine learning, namely the support vector machines (SVM) method, for which the input represents basic information and parameters of bridges, available in the early stage of projects. Several SVM-based regression models were investigated with the use of data collected for a number of bridge construction projects completed in Poland. Having finished the machine learning and testing processes, five of the models, of satisfying knowledge generalization ability and comparable performance, were preselected. The final selection of the best model was based on the comparison and analysis ability to predict bridge construction costs with accuracy appropriate for the early stage of projects. The general testing metrics of the finally selected model, named BCCPMSVR2, were as follows: root mean square error: 1.111; correlation coefficient of real-life bridge construction costs and costs predicted by the model: 0.980; and mean absolute percentage error: 10.94%. The research resulted in the development and introduction of an original model capable of providing early estimates of bridge construction costs with satisfactory accuracy.

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Developing an SVM based risk hedging prediction model for construction material suppliers

Cited by 41 publications

References 30 publications

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Optimization of Chemical Fungicide Combinations Targeting the Maize Fungal Pathogen, <italic>Bipolaris maydis</italic>: A Systematic Quantitative Approach

On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

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