Conceptual Estimation of Construction Costs Using the Multistep Ahead Approach

Dursun, Onur; Stoy, Christian

doi:10.1061/(asce)co.1943-7862.0001150

Cited by 34 publications

(12 citation statements)

References 26 publications

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“…Five of them did not create additional models to compare results. Sonmez [55] and Dursun and Stoy [73] compared their accuracy with models developed with ANN, and Li et al [74] compared an MRA model with the Unit Area Cost method. Lowe et al [52] and Ji et al [71] utilised techniques of Stepwise Regression and Principal Component Analysis to select the optimal parameters, respectively.…”

Section: Multiple-regression Analysismentioning

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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Section: Multiple-regression Analysismentioning

confidence: 99%

Predictive Analytics for Early-Stage Construction Costs Estimation

et al. 2022

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“…Of these, MCS requires the mean and standard deviation of historical cost data from similar projects to develop a probability distribution function (Chang & Ko, 2017) -a constraint that restricts the application of MCS to power plant projects, which are less frequent and with limited access to cost datasets (Islam et al, 2021). Williams and Gong (2014) use cost data of 92 building projects with an integrated ANN and support vector machine (SVM) for better PC prediction accuracy, and Dursun and Stoy (2016) use 657 building projects for PC prediction by applying an ANN-based multistep ahead approach. While ANN performs better than the multiple regression model (Hashemi et al, 2019), it involves a greater amount of trial and error.…”

Section: Introductionmentioning

confidence: 99%

Budget and Cost Contingency Cart Models for Power Plant Projects

Arifuzzaman

Gazder

Islam

et al. 2022

Journal of Civil Engineering and Management

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Cost overruns are a ubiquitous feature of construction projects, and realistic budgeting at the development stage plays a significant role in their control. However, the application of existing models to budgeting for power plant projects is restricted by the limited amount of project-specific cost data available. This study overcomes this by using a Classification and Regression Tree (CART) approach involving mixed methods of website visits, document study, and expert opinion to predict the amount of project cost (PC) and cost contingency (CC) needed to cover probable cost increases by the use of models containing readily available project attributes and national economic parameters at the project development stage. The modeling process is demonstrated and tested with a case study involving 58 Bangladeshi power plant projects – producing average absolute errors ranging from 0.7% to 1.7% and enabling project cost, inflation rate, and GDP to be identified as significant factors affecting PC and CC modeling. The approach can be applied to predict the PC during preliminary budgeting and selecting a project type and location aligned to the country’s economic status and policy-making strategies, thus facilitating further investment decisions.

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“…Based on 657 building projects in Germany, a multistep ahead approach is conducted to increase the accuracy of the model's prediction (Dursun and Stoy, 2016). Analytic hierarchy process (AHP) has incorporated into CBR to build a reliable cost estimation model for highway projects in South Korea (Kim et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

Intelligent methodology for project conceptual cost prediction

Elmousalami

2019

Heliyon

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Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision makers. Several methodologies exist to develop a conceptual cost model. However, many gaps exist in the current methodologies such as depending only on experts ‘opinions and questionnaire survey to identify the project features, key cost drivers and developing deterministic predictive models without taking uncertainty nature into consideration. The main contribution of this study is developing an intelligent methodology for predicting the project cost at the conceptual stage. The proposed methodology can automatically identify key cost drivers and maintain uncertainty to predicted cost. Field canals improvement projects (FCIPs) are used as a case study to validate the proposed methodology. The selected methodology has applied quantitative approaches to identify the key cost drivers. In addition, the methodology has applied a genetic fuzzy model that automatically generates fuzzy rules to automatically predict the conceptual cost. Moreover, the results show a superior performance of the genetic fuzzy model than the traditional fuzzy model. In addition, this study presents a publicly open dataset for FCIPs to be used for future models validation and analysis.

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Conceptual Estimation of Construction Costs Using the Multistep Ahead Approach

Cited by 34 publications

References 26 publications

Predictive Analytics for Early-Stage Construction Costs Estimation

Predictive Analytics for Early-Stage Construction Costs Estimation

Budget and Cost Contingency Cart Models for Power Plant Projects

Intelligent methodology for project conceptual cost prediction

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