Cost Estimation Of Construction Projects Research Articles

Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis

ABSTRACT This study focuses on addressing housing gaps in populous southern countries by emphasizing the importance of accurate early cost estimation in construction projects. It compares individual cost prediction models (Decision Tree, BP Neural Network, and Support Vector Machine) with combined prediction models (BP-DT and BP-SVM) for high-rise building cost prediction. The study employs interpretative analysis methods such as the Shapley additive explanation method, partial dependence plots, and individual conditional expectation plots to explore feature interplay and improve model transparency. Results show that the BP-SVM combination algorithm significantly reduces Mean Absolute Error (MAE) compared to other models. Key factors influencing cost estimation include “Estimated project duration” and “Ground floor area”, while others like “Below-ground floors” have minimal impact. The study highlights the interaction of various feature groups on prediction outcomes. Through feature interaction analysis, it is found that the Estimated project duration and Building architecture can have some negative correlation interaction. Estimated project duration and Interior decoration may have to cancel effects Overall, it contributes to construction management by aiding investors in better-assessing project profitability, thereby enhancing investment decision efficiency and quality.

Applying Building Information Modelling (BIM) Technology in Pre-Tender Cost Estimation of Construction Projects: A Case Study in Iran

The pre-tender cost estimation serves as the foundation for determining the project cost in the early stages and is crucial for all parties involved in the tendering process. It is expected to be highly accurate. However, industry surveys have advocated that in the Iranian construction industry, the pre-tender estimated costs of construction projects are not sufficiently accurate during the bidding stage. Building information modelling (BIM) technology is a modern digital tool deployed in the construction industry that has seen substantial growth of application. It employs useful tools in different sectors and has extended its involvement in various stages of the project lifecycle, including feasibility studies, planning, design, construction, and operation and maintenance. This study examines and assesses the status of BIM development in Iran for its applicability in the pre-tender cost estimating process in construction projects. The findings of this study indicate that while there is a limited number of Iranian building projects’ BIM models that possess the required quality and capabilities for BIM-based cost estimation, the results obtained are more precise when compared to existing manual methods. Additionally, utilising BIM-based cost estimation significantly reduces the time required for this process by enhancing the speed of cost estimation operations. An innovative evaluation framework for assessing the quality and functionalities of BIM models is presented, which improves the accuracy of cost estimation before the bidding process in Iran’s construction industry. The research findings demonstrate the enhanced accuracy and effectiveness of BIM as compared to conventional approaches. It also suggests the incorporation of AI technology for model evaluation, which has the potential to become an established global industry standard.

Improvement of pricing in the construction industry: Integrating information modeling technologies and transitioning regions to the resource index method

Ensuring accurate cost estimation for project implementation, while accounting for regional specifics, is a crucial task for pricing practices in the construction industry. To address this challenge, the transition from the base index method with cost item indexation to the resource index method for estimating project costs is planned in the 1st quarter of 2023. Additionally, starting from 2024, the mandatory use of information modeling technologies for project design will be implemented. This article examines the rationale behind implementing modifications in the pricing system, outlines the algorithm for estimating costs using the resource index method, identifies the advantages and challenges associate with this new approach, and highlights the prospects of integrating information modeling technologies. The study employs comparative and statistical analysis, as well as methods of synthesis and systematization of information. The analysis of data from the Federal State Information System of Pricing in Construction (TsS FSIS) focuses on regions that have already transitioned to the resource index method. On the basis of the research findings, the article reveals the issues related to the adoption of alternative cost estimation methods in a region. It also proposes approaches for enhancing the pricing system to effectively apply the resource index method for cost estimation in construction projects, while incorporating the use of information modeling technologies (IMT).

DATA QUALITY ISSUES THAT HINDER THE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK (ANN) FOR COST ESTIMATION OF CONSTRUCTION PROJECTS IN MALAYSIA

The Artificial Neural Network (ANN) which is one of the Artificial Intelligence (AI) tools, has been identified as a great technique to be used for construction cost estimation in the project. With the optimum quality of data input into the ANN model, it could produce an optimum and reliable cost estimation output. Nonetheless, the construction industry is lack of breadth and depth of data that is required as input into ANN. Though many online databases have been made available for data consumers, data quality problems remain unresolved. Thus, this study aims to identify data quality issues that can hinder the implementation of ANN for cost estimation of a construction project. A literature review and semi-structured interview were employed for the data collection of this research. The content analysis method was used to analyse the information obtained through the literature review. Meanwhile, the data collected from the semi-structured interview with nine (9) respondents were analysed using both content analysis and descriptive statistics analysis methods. The findings revealed six data quality issues that can hinder the ANN implementation for cost estimation of construction projects in Malaysia which are inaccurate data, outdated data, data access barriers, insufficient data, noise in training data, and data input degree of influence. Academically, this study contributes to the body of knowledge about the implementation of ANN for cost estimation of construction projects in Malaysia.
 
 Keywords: Artificial Neural Network, Artificial Intelligence, Cost Estimation, Data Quality.

A new model for cost estimation construction project using Hybrid importance regression ensemble method

Cost estimating entails gathering and evaluating historical data, as well as using quantitative models, methodologies, tools, and databases to forecast the cost of a program in the future. At the early stages of the building design process, the cost is considered one of the most important elements in making decisions. During the design phase of a project, cost estimating is quite essential. To compelet a construction project successfully, it is critical to design a usable model and method for cost estimation in construction projects. For the above reason, this study has developed a hybrid method to conduct an accurate cost estimation in construction projects in Iraq. This study also conducted a rigorous survey to find five main influential factors with thirty-six sub-factors in the Iraqi market. It was evaluated through previous studies, questionnaires, and surveys of twenty projects to build a matrix factors database for construction projects. This work gathered the construction cost factors from relevant research and expert views. In the second step, the researcher ranked the factors within the Importance Regression Ensemble Method then the K-Nearest Neighbor Method was applied to specify the effect of the near-effective factors on the cost. The outcome of this study will be helpful to construction professionals in estimating a construction project effectively.

Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation

Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating.

A model utilizing the artificial neural network in cost estimation of construction projects in Jordan

PurposeCost 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.Design/methodology/approachCost 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.FindingsThe models achieved average cost estimation accuracy of 98, 98 and 97% in the detailed, schematic and concept design stages, respectively.Research limitations/implicationsThis paper formulated the aims and objectives to be applicable only in Jordan using historical data of building projects.Originality/valueThe 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.

An integrated BIM-based approach for cost estimation in construction projects

PurposeAnalyzing 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.Design/methodology/approachThis 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.FindingsTo 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.Practical implicationsThe 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.Originality/valueUnlike 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.

Improving cost estimation in construction projects

The accurate cost estimate is one of the success keys to construction projects. The influence of inaccurate cost estimation on construction projects is critical. There is no consensus among researchers about the most important factors influencing the cost estimate accuracy. The study’s objective is to get an accurate cost estimate because the precise cost estimate keeps all parties focused on delivering the project within the budget. This objective is achieved by collecting the influential factors from the literature review and creating a powerful arithmetical model of cost estimation. Twenty-nine factors were collected by reviewing many researches. A questionnaire was prepared and then was examined by fourteen project managers. Pareto technique was conducted to get the foremost influential factors. Based on the Pareto technique, the 29 factors were reduced to nine. Then, a powerful arithmetical model was created in this study to get an accurate cost estimate. Finally, fourteen finished projects were selected to be case studies. The cost variance percentages were between 1% and 15% for each case study. By using the model, the estimated contract value of each case study was recalculated and, the cost variance percentages were between 0.5% and 0.8% for each case study.

Modelling Construction Site Cost Index Based on Neural Network Ensembles

Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide‐ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

A new model for estimation of project total cost in construction projects

This paper presents a new framework for the cost estimation of construction projects concerning the significant issues that contractors may face through the life cycle of the projects. The proposed approach is basically constructed on the basis of cost estimation process in the earned value management (EVM) technique. However, it attempts to resolve the present shortcomings in EVM estimation process and take into the consideration the cost-related issues so-called financial issues such as delay in client payment, and the time value of money. Furthermore, in order to cope with the uncertain conditions of real situations, the presented model takes the advantage of fuzzy sets theory which is a well-known method in dealing with the situations where the uncertainty arises. The proposed approach not only extends the theoretical framework of EVM but also gives a real insight into the project future performance. Finally, ten illustrative cases related to construction projects are provided to compare the obtained results of proposed model with the results of the EVM estimation process and to demonstrate how the model can be implemented in real projects.

The Challenges of Nonparametric Cost Estimation of Construction Works with the use of Artificial Intelligence Tools

Nonparametric cost estimation in construction projects with the use of artificial networks is presented as suitable mainly for the early estimates. These conceptual estimates are based on the variables – namely cost predictors that characterize the project or a facility. Data gathered on the basis of completed projects are combined together and applied to the current project cost estimation process. The aim of the paper is to discuss the opportunities and challenges of the approach based on the artificial intelligence tools to cost estimation of construction works. The proposed approach is based on the concept of nonparametric cost estimation and application of artificial neural networks. The author's idea and intention is to transfer the mechanisms of nonparametric cost estimating to the level of construction works. Neural networks, due to their general capabilities, seem to be a good tool to aid the proposed approach. The paper contains discussion of the proposed approach and its applicability.

Drivers of the accuracy of developers’ early stage cost estimates in residential construction

Purpose – Preliminary cost estimates for construction projects are often the basis of financial feasibility and budgeting decisions in the early stages of planning and for effective project control, monitoring and execution. The purpose of this paper is to identify and better understand the cost drivers and factors that contribute to the accuracy of estimates in residential construction projects from the developers’ perspective. Design/methodology/approach – The paper uses a literature review to determine the drivers that affect the accuracy of developers’ early stage cost estimates and the factors influencing the construction costs of residential construction projects. It used cost variance data and other supporting documentation collected from two case study projects in South East Queensland, Australia, along with semi-structured interviews conducted with the practitioners involved. Findings – It is found that many cost drivers or factors of cost uncertainty identified in the literature for large-scale projects are not as apparent and relevant for developers’ small-scale residential construction projects. Specifically, the certainty and completeness of project-specific information, suitability of historical cost data, contingency allowances, methods of estimating and the estimator’s level of experience significantly affect the accuracy of cost estimates. Developers of small-scale residential projects use pre-established and suitably priced bills of quantities as the prime estimating method, which is considered to be the most efficient and accurate method for standard house designs. However, this method needs to be backed with the expertise and experience of the estimator. Originality/value – There is a lack of research on the accuracy of developers’ early stage cost estimates and the relevance and applicability of cost drivers and factors in the residential construction projects. This research has practical significance for improving the accuracy of such preliminary cost estimates.

Factors influencing accuracy of construction project cost estimates in Pakistan: Perception and reality

Construction projects are unique as they are priced before being constructed. Therefore, accuracy of construction project cost estimates is pivotal in project success. Several studies have determined factors influencing the accuracy of cost estimates. Comparatively less information is available for construction industries in developing countries. This study identified factors influencing the accuracy of cost estimates for construction projects in Pakistan. Cost data was collected for 46 completed construction projects. Four dimensions 60 factors were considered. Dimensions included: project characteristics, project team, estimating procedures, and (costing related) information items. A rigorous analysis framework based on principal component analysis and regression analysis was used. Involvement of the contractor in the estimation procedure and labour rates were found to be the most significant factors. Early involvement of the contractor can result in realistic estimates through timely input from subcontractors, materials suppliers and manufacturers among others. This also helps in controlling labour costs as most of the construction projects in Pakistan employ labour through subcontractors.

BIM 기반의 공간객체를 이용한 물량산출 정확성 분석

After being introduced, Building Information Modeling (BIM) has been actively applied to the cost estimation of construction projects, and various studies on BIM based quantity take-off have been carried out. In practice, however, these calculations take considerable time, because BIM based quantity take-off is further conducted along with 2D-based quantity take-off. Studies on the quantity take-off using BIM spatial objects have been carried out on early stages of projects, but how this method differs from the existing quantity take-off method and how accurate it is in comparison have rarely been verified. Therefore, by comparing 2D based quantities with quantities through BIM spatial objects, this study analyzed the accuracy of quantity take-off using BIM spatial objects. To this end, the properties of BIM spatial objects and quantity calculable spatial types were analyzed, and existing 2D-based quantities and quantities extracted from BIM spatial objects were compared through a case study. As a result, the quantity of spatial objects found to be more by about 7.13% in 0.05% and therefore, this difference should be considered during quantity take-off using BIM spatial objects. Through the results of this study, we can improve the accuracy of quantity take-off using BIM spatial objects in the early stage of a construction project.

Cost Estimation of Construction Project Using Fuzzy Neural Network Model Embedded with Modified Particle Optimizer

A fuzzy neural network (FNN) model embedded with a modified particle swarm optimizer (MPSO) is proposed in this study for cost estimation of construction projects. The proposed method has advantages over traditional FNN approaches in ways of generalization ability and automatic parameter optimization. Comparative studies show that this improved model is also superior to those of BP (back propagation) neural network, PSO-BP and PSO-FNN. Effectively improved accuracy of the developed model in cost estimation of construction project was discussed in case studies. This study provides a base for decision-making in the management of construction project.

Bayesian Model for Cost Estimation of Construction Projects

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.

Framework of the approximate cost estimating model for river dredging construction

Recently, construction on the Four River Restoration Project began in South Korea. The project includes the maintenance of river embankments, the development of river areas, and river dredging. Accordingly, considerable attention is being paid to the necessary budget and estimation of construction costs. However, only a few studies have been carried out for the cost estimation of construction projects for river development or maintenance. In particular, approximate construction cost estimations for river dredging have not been studied. The lack of sufficient studies on estimating the approximate construction cost of river dredging has caused some doubt about the validity of the project’s estimated construction cost. This study proposes a framework of an approximate cost estimating model for river dredging construction. For this purpose, the construction operation process, type of river section, and the combination of equipment employed for river dredging were analyzed from the historical data of river dredging projects conducted in South Korea. The appropriateness of the analysis was examined through interviews with experts in the field. The framework of the approximate cost estimating model for river dredging construction proposed in this study provides a basis from which to develop a more accurate construction cost estimation of river dredging at the planning stage.

Sliding-Scale Contingency for Project Development Process

Initial cost estimates for construction projects can be prepared in the planning phase of project development and are revised through the programming and design phases of project development as more project information becomes available. These estimates are characterized by uncertainty, especially early in project development, because of the developing definition of the project's scope. If uncertainties are not estimated and managed appropriately, the result is most often a reduction in the accuracy of cost estimates. Potential project risks should be identified early in project development. The risks represent uncertain events or conditions that, if they occur, have a negative effect on a project's objectives. A total project cost estimate is composed of a base estimate of known costs and a contingency estimate of risk costs. A recent NCHRP study reviewed current practice and discovered that state highway agencies (SHAs) estimate contingency by using a range of methods from traditional top-down percentages to detailed bottom-up risk analyses. The project also found that a majority of the SHAs’ methods lack structure, consistency, or both in applications across projects. This study developed a top-down sliding-scale contingency estimation technique that takes into account factors of project complexity and phases of project development. Sliding-scale contingency graphs were developed for three levels of project complexity by using the Delphi method to elicit the expert opinions in the application of contingency on highway projects, with the primary aim of providing a consistent rationale for setting contingency on highway projects.