Estimating time performance for building construction projects in Vietnam

Le‐Hoai, Long; Lee, Young Dai; Nguyen, Anh Tuan

doi:10.1007/s12205-013-0862-3

Cited by 32 publications

(24 citation statements)

References 38 publications

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“…The training states were developed used the Levenberg-Marquardt (LM) using the back-propagation algorithm comparing the output and the target of the analysis using the ANN. The performance illustrates the best fit value for the selected epoch value [7]. The Mean Square Error (MSE) shows the deviation of the predicted value from the real data.…”

Section: Resultsmentioning

confidence: 99%

“…The research demonstrated that the use of artificial intelligence in the field of construction engineering is far from ideal. Further research, as illustrated the neural networks provide a robust system that can be used as a complementary computational system to the conventional expert systems used for forecasting costs and project durations [7]. Jain et al introduce the aspect of neural networks as a prospective design and conceptual tool used to improve automation ideal in the engineering and construction industry.…”

Section: Related Workmentioning

confidence: 99%

“…Jain et al introduce the aspect of neural networks as a prospective design and conceptual tool used to improve automation ideal in the engineering and construction industry. The inclusion of the underlying neural network in their research shows that there are possibilities of incorporating neural networks in expert systems for enhancing efficiency in the intelligent systems [7]. This research dwells on the inaugural use of neural networks in the construction industry, but for this research, we create a comprehensive ANN model using three vital AI aspects to compute the time for completion of a residential project.…”

Section: Related Workmentioning

confidence: 99%

“…The use of artificial neural networks Computing the conceptual estimates to a substantial extent would decrease the time and the cost for data processing. The application of databases in the assessment of the viability of a prospective project is significant in the provision of historical data on the real cost and completion time of a successful project [7]. The database would make the estimations more accurate for both the cost and duration of the potential project, thus improving the decisions made by the contractor in taking up the project.…”

Section: Introductionmentioning

confidence: 99%

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Guess the time of implementation of residential construction projects using neural networks ANN

AL-Zubaidi

Yas

Abbas³

2019

PEN

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The construction duration of residential projects, especially in building processes, significantly impact the business of a construction company. The balance between the planned cost, direct cost, and overheads directly depend on the precision of the implementation phase of the project. The application of the artificial neural network (ANN) to predict the duration of implementation of a residential construction project from the pre-design stage to completion is comprehensively discussed in this research. The study applies the backpropagation (BP) network made of nodes for error evaluation of the training states. Further, the proposed system illustrated that the artificial neural network (ANN) satisfy the three crucial criteria (cost, quality, and time) used for the evaluation of projects. The ANN provided accurate data for the training and estimation of, the duration of a residential construction project with adequate resources of implementation. The performance of the results for the ANN at 105 iteration shows that the prediction was 99.841% accurate for the overall system. The best fit occurred at the 99th epoch with an MSE of 0.10286.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Guess the time of implementation of residential construction projects using neural networks ANN

AL-Zubaidi

Yas

Abbas³

2019

PEN

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“…Le-Hoai et al [14] applied multiple regression analysis techniques to integrate significant variables including subcontractor selection to predict project length. Park [15] investigated critical success factors for whole life performance assessment, placing the identified factors into a criteria matrix to aid decision making for selecting subcontractors at the bid stage.…”

Section: Introductionmentioning

confidence: 99%

Predicting Subcontractor Performance Using Web‐Based Evolutionary Fuzzy Neural Networks

2013

The Scientific World Journal

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Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

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RETRACTED ARTICLE: Probabilistic methods and neural networks in structural engineering

Refonaa

Huy

Trung

et al. 2022

Int J Adv Manuf Technol

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Estimating time performance for building construction projects in Vietnam

Cited by 32 publications

References 38 publications

Guess the time of implementation of residential construction projects using neural networks ANN

Guess the time of implementation of residential construction projects using neural networks ANN

Predicting Subcontractor Performance Using Web‐Based Evolutionary Fuzzy Neural Networks

RETRACTED ARTICLE: Probabilistic methods and neural networks in structural engineering

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