A hybrid approach for a cost estimate of residential buildings in Egypt at the early stage

Badawy, Mohamed

doi:10.1007/s42107-020-00237-z

Cited by 16 publications

(6 citation statements)

References 30 publications

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“…Author's future work is focused on the comparison between this proposed model and another ANN hybridized with a metaheuristic such as GA, Bees algorithm, Ant Colony algorithm, or Artificial Bee Colony algorithm. e author Badawy [51] has proposed hybrid model for estimation of the cost of residential buildings in Egypt. Real data were used from 174 real residential projects.…”

Section: Resultsmentioning

confidence: 99%

Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

Car-Pušić

Petruseva

Pancovska

et al. 2020

Advances in Civil Engineering

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A model for early construction cost prediction is useful for all construction project participants. This paper presents a combination of process-based and data-driven model for construction cost prediction in early project phases. Bromilow’s “time-cost” model is used as process-based model and general regression neural network (GRNN) as data-driven model. GRNN gave the most accurate prediction among three prediction models using neural networks which were applied, with the mean absolute percentage error (MAPE) of about 0.73% and the coefficient of determination R2 of 99.55%. The correlation coefficient between the predicted and the actual values is 0.998. The model is designed as an integral part of the cost predicting system (CPS), whose role is to estimate project costs in the early stages. The obtained results are used as Cost Model (CM) input being both part of the Decision Support System (DSS) and part of the wider Building Management Information System (BMIS). The model can be useful for all project participants to predict construction cost in early project stage, especially in the phases of bidding and contracting when many factors, which can determine the construction project implementation, are yet unknown.

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Section: Resultsmentioning

confidence: 99%

Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

Car-Pušić

Petruseva

Pancovska

et al. 2020

Advances in Civil Engineering

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“…(6) as:where Robs−i is the observed R of ith contact, and Rcom−i is the computed R of the ith case (contract) by the ANN model. Badawy (2020) considers 0.2 as a threshold value for the allowable and not allowable relative error. In this paper, the data with an APEi value of more than 0.2 is considered abnormal and deleted from the train data group.…”

Section: Methodsmentioning

confidence: 99%

Integrated artificial and deep neural networks with time series to predict the ratio of the low bid to owner estimate

Almohsen,

Alsanabani,

Alsugair

et al. 2023

ECAM

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PurposeThe variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.Design/methodology/approachThis paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).FindingsThe main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.Practical implicationsThe owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.Originality/valueThis study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

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“…On the other hand, Elhag and Boussabaine [48] compared two ANNs, using 13 parameters and using only four. [52] 2004 ANN Architecture GA Kim et al [89] 2005 ANN Architecture GA Cheng et al [90] 2009 ANN Architecture FL/GA Cheng et al [56] 2010 ANN Architecture High Order NN/FL/GA Sonmez [91] 2011 Input parameters/ANN Architecture Bayesian regularisation/Bootstraps prediction intervals Rafiei and Adeli [92] 2018 Model architecture DBM combination Jumas et al [93] 2018 Input parameters MRA Badawy [94] 2020 Model architecture MRA combination…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predictive Analytics for Early-Stage Construction Costs Estimation

et al. 2022

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Low accuracy in the estimation of construction costs at early stages of projects has driven the research on alternative costing methods that take advantage of computing advances, however, direct implications in their use for practice is not clear. The purpose of this study was to investigate how predictive analytics could enhance cost estimation of buildings at early stages by performing a systematic literature review on predictive analytics implementations for the early-stage cost estimation of building projects. The outputs of the study are: (1) an extensive database; (2) a list of cost drivers; and (3) a comparison between the various techniques. The findings suggest that predictive analytic techniques are appropriate for practice due to their higher level of accuracy. The discussion has three main implications: (a) predictive analytics for cost estimation have not followed the best practices and standard methodologies; (b) predictive analytics techniques are ready for industry adoption; and (c) the study can be a reference for high-level decision-makers to implement predictive analytics in cost estimation. Knowledge of predictive analytics could assist stakeholders in playing a key role in improving the accuracy of cost forecast in the construction market, thus, enabling pro-active management of the project owner’s budget.

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A hybrid approach for a cost estimate of residential buildings in Egypt at the early stage

Cited by 16 publications

References 30 publications

Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

Integrated artificial and deep neural networks with time series to predict the ratio of the low bid to owner estimate

Predictive Analytics for Early-Stage Construction Costs Estimation

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