A support vector machine model for contractor prequalification

Lam, K. C.; Palaneeswaran, Ekambaram; Yu, Chen-yun

doi:10.1016/j.autcon.2008.09.007

Cited by 76 publications

(34 citation statements)

References 52 publications

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“…The adequate selection of suitable contractors is directly related to construction project success and the achievement of specified objectives, therefore contractor selection constitutes a critical decision for any project manager [1,2].…”

Section: Introductionmentioning

confidence: 99%

A fuzzy multi-criteria decision-making model for construction contractor prequalification

Nieto-Morote

Ruz-Vila

2012

Automation in Construction

117

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Selecting an appropriate contractor is essential for the success of any construction project. Contractor prequalification procedure makes it possible to admit for tendering only competent contractor. Prequalification is a multi-criteria decision problem that is, in essence, largely dependent on the uncertainty and vagueness in the nature of construction projects and subjective judgement of the decision-maker. This paper presents a systematic prequalification procedure, based on Fuzzy Set Theory, whose main differences and advantages in comparison with other models are the use of an algorithm to handle the inconsistencies in the fuzzy preference relation when pair-wise comparison judgements are used and the use of linguistic assessment or exact assessment of performance of the contractors on qualitative or quantitative criterion, respectively. Finally, a case study for the rehabilitation project of a building at Technical University of Cartagena is presented to illustrate the use of the proposed model and to demonstrate its effectiveness.

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Section: Introductionmentioning

confidence: 99%

A fuzzy multi-criteria decision-making model for construction contractor prequalification

Nieto-Morote

Ruz-Vila

2012

Automation in Construction

117

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“…This technique classifies data with distinctive class labels by identifying a set of support vectors from the set of training data, and these support vectors can help to construct the class decision boundary (Dibike et al 2001;Lin et al 2011). One advantage of the SVC is that the method is very effective in the situation which the size of data samples is limited (Lam et al 2009;Pal 2006). Moreover, the input patterns in such high-dimensional feature spaces can be classified by a hyperplane constructed by the SVC.…”

Section: Introductionmentioning

confidence: 98%

Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier

Cheng

Hoang

2015

Nat Hazards

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This research proposes a novel bee colony optimized support vector classifier (BeeSVC) for predicting typhoon-induced slope collapses. The BeeSVC employs the support vector classifier (SVC) as a machine learning method to classify a slope into two classes: ''stable slope'' and ''collapsed slope.'' Furthermore, the artificial bee colony algorithm is used as a metaheuristic to determine the hyper-parameters of the SVC appropriately. The contribution of the proposed method to the body of knowledge is multifold. First, the combined framework of the BeeSVC allows the assessment process to be operated automatically. Second, since the number of the ''collapsed'' class occupied more than 70 % of the historical cases, a repeated random sub-sampling procedure with the Student's t test is put forward to alleviate the class-imbalanced problem and reliably evaluate the model performance. Third, the mutual information between the input features and the slope performance is employed to reflect the contribution of each feature to the slope failure. Lastly, the superior performance has proved that the BeeSVC can be a very effective tool for decision-makers to forecast typhoon-induced slope collapses.

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“…In general, the SVM technique classifies a data sample by mapping the data points into a high-dimensional feature space and identifying the classification boundary in such space. The advantages of SVM include strong inference capacity, good generalization, fast learning, and accurate prediction [19,20]. This section describes the formulation of the SVM algorithm.…”

Section: Support Vectormentioning

confidence: 99%

An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Tran

Hoang

2014

Journal of Construction Engineering

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Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach-autotuning support vector machine-is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and failure. Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

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A support vector machine model for contractor prequalification

Cited by 76 publications

References 52 publications

A fuzzy multi-criteria decision-making model for construction contractor prequalification

A fuzzy multi-criteria decision-making model for construction contractor prequalification

Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier

An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

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