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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.

Previous studies have highlighted that overheads form a critical part of the total project cost. However, despite this knowledge, the precise estimation of overheads is often neglected in construction projects. This paper aims to examine the reasons for the lack of effort in estimation of overheads.

The study is carried out in three stages. In stage one, an introductory survey is carried to understand the importance of overheads in total project cost. In stage two, a detailed survey is carried out to understand the factors that affect the level of accuracy and compromise made in estimation of overheads using partial least squares structural equation modeling (PLS-SEM). In the final stage, two cases are examined in form of interviews to validate the findings. The model is tested for its reliability, validity and goodness of fit.

The findings of the study suggest that the time and cost spent is a critical issue. Therefore, if the projects feel that the benefit cost ratio for time and cost spend in the process is positive, they estimate the overheads accurately, whereas if they feel that the cost benefit ratio for time and cost spend is negative, they compromise with accurate estimation. Further, there is a lot of subjectivity in defining and processing overheads that leads to a negative impact on the accuracy level in estimation of overheads. The contract type also influences the compromise in estimation.

First, there is scant work that has been carried out on understanding the behavior of overheads and reasons for lack of effort in its accurate estimation in construction projects. Therefore, there are no recent citations in the study. Further, the study being exploratory in nature draws conclusions based on opinion expressed by respondents on survey and interview. Finally, the study is geographically limited as the entire respondent's work in India.

Projects should give due attention to accurate estimation of overheads. Accurate estimation of overheads can help in better control of project margins, thereby serving the profit-maximizing goals of organizations. A conscious effort by industry experts, academicians and researchers can bring some discipline in overhead estimation rather than leaving the critical domain only to thumb rules or experiential assumptions. The regulatory bodies and the project management bodies are advised to come up with some kind of ready reference that can quickly help estimators to arrive at accurate overhead costs.

To the best of knowledge, it is a rare study to exclusively focus on project overhead cost in construction industry and focus on its estimation efforts. The study also uses a robust research process, which improves the reliability and validity of its findings.

The objectives of this paper are to discuss the constraints stemming from the unstructured nature of the cost estimation practice in Turkey and introduce a generic computer aided building cost estimation model based on a cost significant technique for Turkish construction sector public projects in its detailed design phase.

The research design was based on the simplified version of the bill of quantities method and a cost significant estimating model works on a cost database was suggested to overcome the problems. Underlying principles and basic steps of cost estimation based on cost significant work packages was explained by means of manual calculations.

In order to automate the manual building cost estimation process, the software based on cost significant work packages was developed.

The software is currently in the testing phase and is being used for educational purposes. Making use of both public sector and current market prices in the cost estimation process, increasing number of projects stored in the database for more accurate results, estimating costs of different types of projects and calculating the cost significant value factor more precisely by using statistical techniques, those being employed by global cost models are suggested for future research.

As the number of the similar projects in database is increased, the accuracy of the cost estimation is also increased.

Estimators and graduate students can use the software to estimate building cost of public housing projects in its detailed design phase.

The amalgamation of integrated project delivery (IPD) and building information modelling (BIM) is highly recommended for successful project delivery. However, IPD lacks an accurate cost estimation methodology at the “front-end” of projects, when little project information is available. This study aims to tackle this issue, through presenting analytical aspects, theoretical grounds and practical steps/procedures for integrating target value design (TVD), activity-based costing (ABC) and Monte Carlo simulation into the IPD cost structure, within a BIM-enabled platform.

A critical review was conducted to study the status of cost estimation within IPD, as well as exploring methods and tools that can enhance the cost estimation process for IPD. Thereafter, a framework is developed to present the proposed methodology of cost estimation for IPD throughout its entire stages. A case project is used to validate the practicality of the developed solution through comparing the profit-at-risk percentage for each party, using both traditional cost estimation and the proposed solution.

After applying the proposed IPD's cost estimation framework, on a real-life case project, the findings demonstrated significant deviations in the profit-at-risk value for various work packages of the project (approximately 100% of the finishing package and 22% of openings package). By providing a precise allocation of overhead costs, the solution can be used in real-life projects to change the entire IPD cost structure and ensure a fair sharing of risk–rewards among the involved parties in IPD projects.

Using the proposed methodology of cost estimation for IPD can enhance the relationship among IPD's core team members; all revealed financial deficiencies will be considered (i.e. compensation structure, profit pooling), hence enhancing the IPD performance.

This paper presents a comprehensive solution for integrating BIM and IPD in terms of cost estimation, offering three main contributions: (1) an innovate approach to utilise five-dimensional (5D) BIM capabilities with Monte Carlo simulation, hence providing reliable cost estimating during the conceptual TVD stage; (2) mathematical models that are developed through integrating ABC into the detailed 5D BIM to determine the three IPD's cost structure limbs; and (3) a novel mechanism of managing cost saving (rewards) through distinguishing between saved resources from organisation level, to daily task level, to increase trust among parties.

This research aims to develop an automated and optimization algorithms (OAs)-integrated 4D building information modeling (BIM) approach and a prototype and enable construction managers and practitioners to estimate the time of compound elements in building projects using the resource specification technique.

A 4D BIM estimation process was first developed by applying the resource specification and geometric information from the BIM model. A suite of OA including particle swarm optimization, ant colony, differential evolution and genetic algorithm were developed and compared in order to facilitate and automate the estimation process. The developed processes and porotypes were linked and integrated.

The OA-based automated 4D BIM estimation prototype was developed and validated through a real-life construction project. Different OAs were applied and compared, and the genetic algorithm was found as the best performing one. The prototype was successfully linked with BIM timeliner application. By using this approach, the start and finish dates of all object-based activities are developed, and the project completion time is automatically estimated.

Unlike conventional construction estimation methods which need various tools and are error prone and time-consuming, the developed method bypasses the existing time estimation tools and provides the integrated and automated process with BIM and machine learning algorithms. Furthermore, this approach integrates 4D BIM applications into construction design procedures, connected with OA automation.

Project-based industries face major challenges in controlling project cost and completing within the budget. This is a critical issue as it often connects to the main objectives of any project. However, accurate estimation at the beginning of the project is difficult. Scholars argue that project complexity is a major contributor to cost estimation inaccuracies. Therefore, recognising the priorities of acknowledging complexity dimensions in cost estimation across similar industries is beneficial in identifying effective practices to reduce cost implications. Hence, the purpose of this paper is to identify the level of importance given to different complexity dimensions in cost estimation and to recognise best practices to improve cost estimation accuracy.

An online questionnaire survey was conducted among professionals including estimators, project managers, and quantity surveyors to rank the identified complexity dimensions based on their impacts in cost estimation accuracy. Besides, in-depth interviews were conducted among experts and practitioners from different industries, in order to extract effective practices to improve the cost estimation process of complex projects.

Study results show that risk, project and product size, and time frame are the high-impact complexity dimensions on cost estimation, which need more attention in reducing unforeseen cost implications. Moreover, study suggests that implementing a knowledge sharing system will be beneficial to acquire reliable and adequate information for cost estimation. Further, appropriate staffing, network enhancement, risk management, and circumspect estimation are some of the suggestions to improve cost estimation of complex projects.

The study finally provides suggestions to improve cost estimation in complex projects. Further, the results are expected to be beneficial to learn lessons from different industries and to exchange best practices.

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 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.

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.