Cash Flow Prediction for Construction Project Using a Novel Adaptive Time-Dependent Least Squares Support Vector Machine Inference Model

Cheng, Min‐Yuan; Hoang, Nhat‐Duc; Wu, Yuwei

doi:10.3846/13923730.2014.893906

Cited by 25 publications

(18 citation statements)

References 22 publications

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“…In this section of the article, to better evaluate the performance of the GPR model, the ANN [26] and LSSVM [9,27] are employed as benchmark methods. The reasons for selecting these two benchmark models are that ANN is widely accepted as an effective tool for nonlinear function approximation and this algorithm has been successfully employed for predicting concrete strength [10,13]; LSSVM is also an advanced machine learning method featured by high modeling accuracy [28][29][30][31] and it has been recently used for modeling concrete compressive strength [5].…”

Section: Results Comparisonmentioning

confidence: 99%

Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

Hoang

Pham

Nguyen

et al. 2016

Advances in Civil Engineering

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This research carries out a comparative study to investigate a machine learning solution that employs the Gaussian Process Regression (GPR) for modeling compressive strength of high-performance concrete (HPC). This machine learning approach is utilized to establish the nonlinear functional mapping between the compressive strength and HPC ingredients. To train and verify the aforementioned prediction model, a data set containing 239 HPC experimental tests, recorded from an overpass construction project in Danang City (Vietnam), has been collected for this study. Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.

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Section: Results Comparisonmentioning

confidence: 99%

Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

Hoang

Pham

Nguyen

et al. 2016

Advances in Civil Engineering

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“…Pada proyek konstruksi minimalisasi waktu dan biaya merupakan masalah penting, kriteria mendasar, dan faktor kritis untuk menentukan keberhasilan proyek (Kim, An et al 2004, Bayraktar, Hastak et al 2011, Memon, Rahman et al 2012, Cheng and Tran 2014, Zhang and Fan 2014. Masalah waktu dan biaya sangatlah sulit untuk diselesaikan, hal ini dikarenakan tidak memiliki solusi yang unik (Li and Love 1997); selain itu juga dipengaruhi oleh banyak faktor dan ketidakpastian yang bersifat dinamis (Akinci and Fischer 1998, Yang and Wei 2010, Bayraktar, Hastak et al 2011, Marzouk and El-Rasas 2014, Cheng, Hoang et al 2015, Korir 2017. Industri konstruksi selalu dianggap sebagai industri dengan kinerja buruk, hal ini dikarenakan selalu gagal dalam mencapai ketepatan waktu dan biaya (Memon, Rahman et al 2012).…”

Section: Pendahuluanunclassified

Optimized Management Strategy for Construction Projects Considering the Trade-Off of Estimate Schedule and Cost at Completion

Gunawan

Cheng²,

Prayogo³

2019

duts

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Nowadays, the minimization of project time and cost is an important issue. However, time and cost problems are difficult to solve. They are affected by the uncertain factor. Then, the construction project always fails to achieve the effectiveness of time and cost performance. It causes delays and cost overrun. In this research, SOS-NN-LSTM is required to establish the estimate schedule to completion (ESTC) and estimate cost to completion (ECTC) prediction model based on time now performance. Then, the prediction model will be integrated with MOSOS to obtain the optimal prediction value. The integration is needed because there is no direct equation to calculate the ESTC and ECTC. The Pareto curve identified based on the prediction values of MOSOS. The Pareto curve is used to determine the optimal trade-off between project duration and project cost. Then, the indifference curve is used to solve the trade-off problem between estimate schedule at completion (ESAC) dan estimate cost at completion (ECAC) which give the decision-maker preference.

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“…Support vector machines have been successfully applied to many real world classification problems. Examples of support vector machines in construction management include: contractor prequalification decision (Lam et al 2009), project success prediction , contractor default prediction (Tserng et al 2011), cash flow prediction (Cheng, Roy 2011;Cheng et al 2015a), project at completion estimation (Cheng, Roy 2010;Cheng et al 2012;Cheng, Hoang 2014a), conceptual cost estimation (Cheng, Roy 2010), litigation outcome prediction (Mahfouz, Kandil 2012), enterprise resource planning software effort forecasting , dispute prediction (Chou 2012;Chou, Lin 2012;Chou et al 2013Chou et al , 2014, construction cost index estimation (Cheng et al 2013), contractor default prediction (Cheng et al 2014), bridge-maintenance risk score prediction (Cheng, Hoang 2014b), change order productivity loss prediction (Cheng et al 2015b). Despite the success of support vector machines in different construction management related classification problems, to the best of our knowledge, application of these methods have not been explored for bid/no bid decision making, which is the main focus of this study.…”

Section: Support Vector Machinesmentioning

confidence: 99%

A support vector machine method for bid/no bid decision making

Sönmez

Sözgen²

2017

Journal of Civil Engineering and Management

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The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advantage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the support vector machines.

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Cash Flow Prediction for Construction Project Using a Novel Adaptive Time-Dependent Least Squares Support Vector Machine Inference Model

Cited by 25 publications

References 22 publications

Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

Optimized Management Strategy for Construction Projects Considering the Trade-Off of Estimate Schedule and Cost at Completion

A support vector machine method for bid/no bid decision making

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