Evolutionary Fuzzy Hybrid Neural Network for Conceptual Cost Estimates in Construction Projects

Cheng, Min‐Yuan; Tsai, Hsing‐Chih; Sudjono, Erick

doi:10.22260/isarc2009/0040

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Construction cost estimation is a crucial activity for proper functioning of any construction firm (ElSawy et al 2011). The application of ANN in cost estimation has been the subject of many studies (Pearce 1997;Bhokha and Ogunlana 1999;Sonmez 2004;Sodikov 2005;Kim et al 2005;Cheng et al 2009a;Cheng et al 2009b;Arafa and Alqedra 2011;Waziri and Bala 2011;Bala et al 2014). Williams (1994) used the BP algorithm for predicting changes in construction cost indices for one and six months ahead and concluded that the movement of the cost index is a complex problem that is difficult to be predicted accurately using the BP model.…”

Section: Cost Estimationmentioning

confidence: 99%

“…The model achieved an overall estimate error of 10.36% due to the use of GA, the method has a high computing time, this being a disadvantage. Cheng et al (2009b) presented a web based hybrid model incorporating genetic algorithms, fuzzy logic theory and neural networks under a mechanism called Evolutionary Fuzzy Neural Inference Model (EFNIM). However, EFNIM also runs long time due to the use of GA.…”

Section: Evolutionary Neural Network In Constructionmentioning

confidence: 99%

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Artificial Neural Networks in Construction Engineering and Management

Waziri¹,

Bala²,

Bustani³

2017

IJAEC

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Artificial Neural Networks has gained considerable application in construction engineering and management in recent time. Over 100 resources published in refereed journals and conference proceedings were screened and reviewed with the view to exploring the trend and new directions of the applications of different ANN algorithms. The study revealed successful applications of ANNs in cost prediction, optimization and scheduling, risk assessment, claims and dispute resolution outcomes and decision making. It was observed that ANN have been applied to problems that are difficult to solve with traditional mathematical and statistical methods. The integration of ANN with other soft computing methods like Genetic Algorithm, Fuzzy Logic, Ant Colony Optimization, Artificial Bee Colony and Particle Swarm Optimization were also explored which generally indicated better results when compared with conventional ANNs. The study provides comprehensive repute of ANN in construction engineering and management for application in different areas for improved accuracy and reliable predictions.

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Section: Cost Estimationmentioning

confidence: 99%

Section: Evolutionary Neural Network In Constructionmentioning

confidence: 99%

Artificial Neural Networks in Construction Engineering and Management

Waziri¹,

Bala²,

Bustani³

2017

IJAEC

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“…The traditional estimating methods as opined by Cheng and Wu (2005), Lowe et al (2006), Marzok et al (2008), have failed to cope with the problems of uncertainties and accuracy. In the light of this, Lowe et al (2006) and Cheng et al (2009aCheng et al ( , 2009b stressed that there is need to provide a more accurate and robust construction cost forecast. Therefore, it is essential to have efficient estimating methods that will replace the current approaches, which will address the need of speed, accuracy, reliability and cut down uncertainty to the minimum.…”

Section: Significance Of the Researchmentioning

confidence: 99%

“…The application of ANN in cost estimation has been the subject of many researches (Pearce, 1997;Adeli and Wu, 1998;Bhokha and Ogunlana, 1999;Sonmez, 2004;Kim et al, 2005;Sodikov, 2005;Cheng et al, 2009aCheng et al, , 2009bArafa and Alqedra, 2011). Bhokha and Ogunlana (1999) developed an ANN model for predicting the construction costs of building projects in Thailand at the pre-design stage.…”

Section: Ann and Cost Estimationmentioning

confidence: 99%

A computer-based cost prediction model for institutional building projects in Nigeria

Bala

Bustani

Waziri

2014

Journal of Engineering, Design and Technology

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Purpose – The purpose of this study was develop a computer-based cost prediction model for institutional building projects in Nigeria through the use of artificial neural network (ANN) technique. The back-propagation network learns by example and provides good prediction to novel cases. Design/methodology/approach – The input variables were derived from related works with modification and advices from professionals through a field survey. Two hundred and sixty completed project data were used for training and development of the ANN model. Back-propagation algorithm using the gradient descent delta learning rule with a learning coefficient of 0.4 was used. The input layer of the model comprised of nine variables; building height, compactness of building, construction duration, external wall area, gross floor area, number of floors, proportion of opening on external walls, location index and time index. Findings – Several multi-layer perceptron networks were developed with varying architecture from which the network 9-7-5-1 was selected. The performance of the model over the validation sample revealed that the model has a mean absolute per cent error of 5.4 per cent and average error of prediction of −2.5 per cent over the sample. The ANN model was considered to be effective for construction cost prediction. Research limitations/implications – The model may not be suitable for other building types because of the uniqueness of such facility even though significant difference is not anticipated for buildings such as commercial and residential. The models were evaluated based on the prediction errors; other means of evaluation were not used. Originality/value – The study thus provides a simple, yet effective means of predicting construction costs of institutional building projects in Nigeria using an ANN model.

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“…Of the many structures available for NNs, the multilayer feed-forward network was chosen for this study because such networks have the ability to deal with complex systems and yet are relatively easy to construct (Hegazy, Ayed 1998;Hunter et al 2012;Ji et al 2009). To train the model, the back-propagation (BP) learning algorithm was used because it has strong classification and generalization capabilities (Cheng et al 2009a;Li, Chen 2012;Xiaokang, Mei 2010). The form of neural network used in this study is common in civil engineering applications.…”

Section: Artificial Neural Network Models For Construction Cost Estimmentioning

confidence: 99%

Predicting freeway pavement construction cost using a back-propagation neural network: a case study in Henan, China

Zhou

Wen

et al. 2014

The Baltic Journal of Road and Bridge Engineering

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The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

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Evolutionary Fuzzy Hybrid Neural Network for Conceptual Cost Estimates in Construction Projects

Cited by 5 publications

References 11 publications

Artificial Neural Networks in Construction Engineering and Management

Artificial Neural Networks in Construction Engineering and Management

A computer-based cost prediction model for institutional building projects in Nigeria

Predicting freeway pavement construction cost using a back-propagation neural network: a case study in Henan, China

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