Proposing a Neural Network Model to Predict Time and Cost Claims in Construction Projects

Yousefi, Vahidreza; Yakhchali, Siamak Haji; Khanzadi, Mostafa; Mehrabanfar, Ehsan; Šaparauskas, Jonas

doi:10.3846/13923730.2016.1205510

Cited by 38 publications

(30 citation statements)

References 42 publications

(38 reference statements)

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“…A study by [43] investigated the use of AI for safety planning during construction. Yousefi et al proposed an AI-based framework for predicting causes of delay in construction [44]. Azari et al utilised AI for guiding the choice of materials that minimise the environmental impact of building envelopes [45].…”

Section: Ai In Buildingsmentioning

confidence: 99%

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

Hammad

2019

Buildings

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Building energy performance tools are widely used to simulate the expected energy consumption of a given building during the operation phase of its life cycle. Deviations between predicted and actual energy consumptions have however been reported as a major limiting factor to the tools adopted in the literature. A significant reason highlighted as greatly influencing the difference in energy performance is related to the occupant behaviour of the building. To enhance the effectiveness of building energy performance tools, this study proposes a method which integrates Building Information Modelling (BIM) with artificial neural network model for limiting the deviation between predicted and actual energy consumption rates. Through training a deep neural network for predicting occupant behaviour that reflects the actual performance of the building under examination, accurate BIM representations are produced which are validated via energy simulations. The proposed method is applied to a realistic case study, which highlights significant improvements when contrasted with a static simulation that does not account for changes in occupant behaviour.

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Section: Ai In Buildingsmentioning

confidence: 99%

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

Hammad

2019

Buildings

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“…Burcar Dunovic et al [58] assessed large infrastructure projects by integrating the risk impact cumulative distribution curve based MCDM approach. Yousefi et al [59] proposed a neural network model for predicting emerging time and cost claims applied to Iranian construction projects. Valipour et al [60] presented a fuzzy cybernetic ANP model for proper identification of public-private partnership project based risks.…”

Section: Previous Studies On Risk Assessment In Construction Projectsmentioning

confidence: 99%

A Hybrid MCDM Technique for Risk Management in Construction Projects

et al. 2018

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Multi-stakeholder based construction projects are subject to potential risk factors due to dynamic business environment and stakeholders' lack of knowledge. When solving project management tasks, it is necessary to quantify the main risk indicators of the projects. Managing these requires suitable risk mitigation strategies to evaluate and analyse their severity. The existence of information asymmetry also causes difficulties with achieving Pareto efficiency. Hence, to ensure balanced satisfaction of all participants, risk evaluation of these projects can be considered as an important part of the multi-criteria decision-making (MCDM) process. In real-life problems, evaluation of project risks is often uncertain and even incomplete, and the prevailing methodologies fail to handle such situations. To address the problem, this paper extends the analytical network process (ANP) methodology in the D numbers domain to handle three types of ambiguous information's, viz. complete, uncertain, and incomplete, and assesses the weight of risk criteria. The D numbers based approach overcomes the deficiencies of the exclusiveness hypothesis and completeness constraint of Dempster-Shafer (D-S) theory. Here, preference ratings of the decision matrix for each decision-maker are determined using a D numbers extended consistent fuzzy preference relation (D-CFPR). An extended multi-attributive border approximation area comparison (MABAC) method in D numbers is then developed to rank and select the best alternative risk response strategy. Finally, an illustrative example from construction sector is presented to check the feasibility of the proposed approach. For checking the reliability of alternative ranking, a comparative analysis is performed with different MCDM approaches in D numbers domain. Based on different criteria weights, a sensitivity analysis of obtained ranking of the hybrid D-ANP-MABAC model is performed to verify the robustness of the proposed method.

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“…The issue of the claim management in the construction projects was solved applying neural network approach [42]. The proposed approach allows for not only classifying and ranking emerging claims, but also to predict the claim frequency in the construction projects.…”

Section: Introductionmentioning

confidence: 99%

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

et al. 2017

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Abstract:Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.

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Proposing a Neural Network Model to Predict Time and Cost Claims in Construction Projects

Cited by 38 publications

References 42 publications

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

A Hybrid MCDM Technique for Risk Management in Construction Projects

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

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