Interval Estimation of Construction Cost at Completion Using Least Squares Support Vector Machine

Cheng, Min‐Yuan; Hoang, Nhat‐Duc

doi:10.3846/13923730.2013.801891

Cited by 48 publications

(27 citation statements)

References 47 publications

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“…A proper setting of these two hyper-parameters can guarantee a good prediction model which can prevent overfitting and generalise the data well (Cheng & Hoang, 2013). In this study, the parameter selection process is formulated as an optimisation problem and the firefly algorithm (FA) is employed to solve the task at hand.…”

Section: Introductionmentioning

confidence: 99%

Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Hoang

2015

Structure and Infrastructure Engineering

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faculty of civil engineering, Institute of research and development, duy tan university, danang, Vietnam ABSTRACT Fibre-reinforced polymer (FRP) provides an alternative reinforcement for concrete flat slabs. This research proposes a hybrid machine learning model for predicting the ultimate punching shear capacity of FRPreinforced slabs. The model employs the least squares support vector machine (LS-SVM) to discover the mapping between the influencing factors and the slab punching capacity. Furthermore, the firefly algorithm (FA), a population-based metaheuristic, is utilised to facilitate the LS-SVM training. A data-set which contains actual tests of FRP-reinforced concrete slabs is utilised to construct and verify the proposed approach. The contribution of this research is to establish a hybrid machine method, based on the LS-SVM and FA algorithms, for meliorating the prediction accuracy of FRP-reinforced slabs' ultimate punching shear capacity. Experimental results demonstrate that the new model has achieved roughly 55 and 15% reductions in terms of Root Mean Squared Error compared with the formula-based and Artificial Neural Network methods, respectively.

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Section: Introductionmentioning

confidence: 99%

Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Hoang

2015

Structure and Infrastructure Engineering

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“…The methods are often universal and can be adapted to any type of object. One of them is the Earned Value Method (EVM or EV) and its extensions (Vandevoorde, Vanhoucke 2006;Webb 2003;Lipke 2003;Cheng, Hoang, 2014). The method is well known (it was developed by the U.S. government in 60's) and is used for simultaneous control of objects with regard to cost and time.…”

Section: Introductionmentioning

confidence: 99%

Application of the Evm Method and Its Extensions in the Implementation of Construction Objects

Ziółkowska¹,

Połoński²

2016

Engineering Structures and Technologies

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Abstract. Surpassing of the planned budget and delivery time is a common feature during the implementation of building investments in Poland. Only a small number of companies use effective methods of project monitoring during investment performance. One of the popular tools used to control projects with regard to cost and time is the earned value method (EVM). There is, however, no detailed guidance how to deploy the method to the specific characteristics and conditions during the execution of building investments. This fact has contributed to this paper, which presents the results of application and adaptation of the earned value method (EVM) and its further extensions in the control of building projects during their execution (with regard to cost and time), prediction of the final duration and costs, and presentation of the effects resulting from calculations. The analyses were performed in terms of the contractor for 5 selected completed and ongoing building projects in Poland.

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“…The ANN approach a major disadvantage is that its training process is achieved through a gradient descent algorithm on the error space, which can be very complex and may contain many local minima [14]. Thus, the ANN training process is likely to be trapped in some local minima and this can deteriorate the financial status prediction performance.…”

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confidence: 97%

Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry

Cheng

Hoang

2015

IEEE Trans. Eng. Manage.

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Construction firms are vulnerable to bankruptcy due to the complex nature of the industry, high competitions, the high risk involved, and considerable economic fluctuations. Thus, evaluating financial status and predicting business failures of construction companies are crucial for owners, general contractors, investors, banks, insurance firms, and creditors. The prediction results can be used to select qualified contractors capable of accomplishing the projects. In this study, a hybrid fuzzy instancebased classifier for contractor default prediction (FICDP) is proposed. The new approach is constructed by incorporating the fuzzy K-nearest neighbor classifier (FKNC), the synthetic minority over-sampling technique (SMOTE), and the firefly algorithm (FA). In this hybrid paradigm, the FKNC is utilized to classify the contractors into two groups ("default" and "nondefault") based on their past financial performances. Since the "nondefault" samples dominate the historical database, the SMOTE algorithm is employed to create synthetic samples of the minority class and therefore alleviates the between-class imbalance problem. Moreover, the FA is employed to determine an appropriate set of model parameters. Experimental results have shown that the proposed FICDP can outperform other benchmark methods.Index Terms-Default prediction, fuzzy instance based classifier, swarm intelligence, synthetic minority over-sampling technique.

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Interval Estimation of Construction Cost at Completion Using Least Squares Support Vector Machine

Cited by 48 publications

References 47 publications

Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Application of the Evm Method and Its Extensions in the Implementation of Construction Objects

Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry

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