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Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural construction cost using design information available in early stages of residential building projects.

Information about residential building projects is collected based on project documents from construction companies with regard to the design parameters and the actual structural construction costs at completion. Storey enclosure method (SEM) is fundamental for determining the building design parameters, forming the potential variables and developing the cost estimation models using regression analysis. Nonparametric bootstrap method is used to establish range estimation for the structural construction cost.

A model which is developed from an integration of advanced SEM, principle component analysis and regression analysis is robust in terms of predictability. In terms of range estimation, cumulative probability-based range estimates and confidence intervals are established. While cumulative probability-based range estimates provide information about the level of uncertainty included in the estimate, confidence intervals provide information about the variability of the estimate. Such information could be very crucial for management decisions in early stages of residential building projects.

This study could provide practitioners with a better understanding of the uncertainty and variability included in the cost estimate. Hence, they could make effective improvements on cost-related management approaches to enhance project cost performance.

Analyzing different scenarios at the design stage of construction projects has always been a challenging task. One of the main parameters that helps owners in making better decisions in designing their buildings is to look after the cost perspective on different design scenarios. Thus, this study aims to propose a semi-automated BIM-based cost estimation approach that enables practitioners to estimate the cost of projects based on different design scenarios by an accurate and agile system.

This study proposes an integrated framework, through which the cost estimation standard of Iran (FehrestBaha) is linked to the materials quantity take-offs (QTO) from BIM models. The performance of the system is based on connecting the classification standards of UniFormat and MasterFormat to the cost estimation standard of FehrestBaha. A BIM-based extension in the Revit environment is developed to automate the cost estimation process.

To evaluate the efficiency of the proposed approach in cost estimation, it is implemented to estimate the cost of the architectural discipline in a real construction project. The results indicate that the proposed BIM-based approach estimated the cost of the architectural discipline with an acceptable level of accuracy.

The proposed approach could be used by practitioners to have an agile and accurate BIM-based cost estimation of different scenarios during design process. The semi-automated system considerably reduces the time of cost estimation in comparison to the traditional manual approaches, particularly in complex structures. Owners are able to easily trace changes in project cost according to any changes in components and materials of the BIM model. Furthermore, the proposed approach provides a practical roadmap for BIM-based cost estimation based on cost estimation standards in different countries.

Unlike the traditional manual cost estimation approaches, the proposed BIM-based approach is not highly dependent on the knowledge of experienced estimators, which therefore facilitates its implementation. Furthermore, automating both QTO process and the required calculations in this approach increases the accuracy of cost estimation while decreasing the probability of human errors or omission occurrence.

Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units.

This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples.

According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation.

The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.

Implementing building information modeling (BIM) in construction projects offers many benefits. However, the use of BIM in project cost management is still limited. This study aims to review the current trends in the application of BIM in project cost management.

This study systematically reviews the literature on the application of BIM in project cost management. A total of 46 related articles were identified and analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method.

Eighteen approaches to applying BIM in project cost management were identified. The approaches can be grouped into cost control and cost estimation. Also, BIM can be applied independently or integrated with other techniques. The integrated approaches for cost control include integration with genetic algorithms, Monte Carlo simulation, lean construction, integrated project delivery, neural network and value engineering. On the contrary, integrated approaches for cost estimation include integration with cost-plus pricing, discrepancy analysis, construction progress curves, estimation standards, algorithms, declarative mappings, life cycle sustainability assessment, ontology, Web-based frameworks and structured query language.

To the best of the authors’ knowledge, this study is the first to systematically review prior literature on the application of BIM in project cost management. As a result, the study provides a comprehensive understanding of the current state of the art and fills the literature gap. Researchers and industry professionals can use the study findings to increase the benefits of implementing BIM in construction projects.

The purpose of this paper is to predict implementation cost contingencies for residential construction projects in flood-prone areas, where floods with storms frequently cause serious damage and problems for people.

Expert interviews are conducted to identify the study variables. Based on bills of quantities and project documents, historical data on residential construction projects in flood-prone areas are collected. Pearson correlation analysis is first used to check the correlations among the study variables. To overcome multicollinearity, principal component analysis is used. Then, stepwise multiple regression analysis is used to develop the cost prediction model. Finally, non-parametric bootstrap method is used to develop range estimation of the implementation cost.

A list of project-related variables, which could significantly affect implementation costs of residential construction projects in flood-prone areas, is identified. A model, which is developed based on an integration of principle component analysis and regression analysis, is robust. Regarding range estimation, 10, 50 and 90 percent cost estimates, which could provide information about the uncertainty levels in the estimates, are established. Furthermore, implementation cost contingencies which could show information about the variability in the estimates are determined for example case projects. Such information could be critical to cost-related management of residential construction projects in flood-prone areas.

This study attempts to predict implementation cost contingencies for residential construction projects in flood-prone areas using non-parametric bootstrap method. Such contingencies could be useful for project cost budgeting and/or effective cost management.

Cost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all projects to establish the project's budget. Confidence in these initial estimates is low, primarily due to the limited availability of suitable data, which leads the construction projects to frequently end up over budget. This paper investigated the efficacy of artificial neural networks (ANNs) methodologies in overcoming cost estimation problems in the early phases of the building design process.

Cost and design data from 104 projects constructed over the past five years in Jordan were used to develop, train and test ANN models. At the detailed design stage, 53 design factors were utilized to develop the first ANN model; then the factors were reduced to 41 and were utilized to develop the second predictive model at the schematic design stage. Finally, 27 design factors available at the concept design stage were utilized for the third ANN model.

The models achieved average cost estimation accuracy of 98, 98 and 97% in the detailed, schematic and concept design stages, respectively.

This paper formulated the aims and objectives to be applicable only in Jordan using historical data of building projects.

The ANN approach introduced as a management tool is expected to provide the stakeholders in the engineering business with an indispensable tool for predicting the cost with limited data at the early stages of construction projects.

Simulation techniques for cost management are useful for modeling uncertainties, making decisions, and improving the accuracy of cost estimation. Despite their usefulness, the application of these techniques in construction projects seems to be uncommon in the construction sector in Malaysia. The purpose of this paper is to determine the application of simulation techniques for cost estimation and control and to assess their influence on project cost performance.

A survey questionnaire was used to collect data from 83 government agencies, consultant firms, and contractor firms in Kuala Lumpur, Malaysia.

The findings revealed that knowledge of respondents and usage of cost simulation techniques in the Malaysian construction industry is low. In addition, main barriers of implementing cost simulation techniques are identified. Cost performance of construction projects in Malaysia is satisfactory; however, there is no association between this performance and the application of simulation techniques.

This paper contributes to construction management field by highlighting the main simulation techniques for cost management and drawing the attention of construction professionals and contractors to implement these techniques in construction projects.

Cost estimating process is an important element within the project life cycle. Comprehensive information, expanded knowledge, considerable expertise, and continuous improvement are needed to obtain accurate cost estimation. The purpose of this paper is to identify the critical factors that affect accuracy of cost estimation and evaluate the degree to which these factors are important from contractors’ and consultants’ viewpoints.

Qualitative and quantitative research approaches were adopted in collecting and analyzing the data, and testing the hypotheses. Based on the literature review, a questionnaire was prepared and then was modified according to the results of face-to-face open-ended interviews conducted with 11 project managers. The final version of the questionnaire was distributed to a random sample of 265 respondents. For analyzing the collected data Kendall’s and Mann-Whitney tests were conducted.

The analysis revealed that there is a strong agreement between contractors and consultants in the ranking of the factors related to consultant, contractor, design parameters, and information. A slightly weak agreement between contractors and consultants was noted regarding the factors related to market conditions (external factors) and factors related to project characteristics. Furthermore, the results show that the top ten factors affecting the accuracy of cost estimate are clear and detail drawings and specification, pricing experience of construction projects, perception of estimation importance, equipment (cost/availability/performance), project complexity, clear scope definition, accuracy and reliability of cost information, site constraints (access, storage, services), material availability, financial capabilities of the client, and availability of database of bids on similar project (historical data).

Offers an original view of the concept of accuracy of cost estimates as it relates to the efficiency of the project relying on both literature review and empirical evidence.

The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy.

A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs.

The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects.

A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample.

This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.