Project Cost Estimation Research Articles

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering.

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

Optimization of software cost estimation model based on biogeography-based optimization algorithm

Estimation of software cost (ESC) is considered a crucial task in the software management life cycle as well as time and quality. Prior to the development of a software project, precise estimations are required in the form of person month and time. In the last few decades, various parametric and non-algorithmic or non-parametric approaches regarding the estimation of software costs have been developed. Among them, the constrictive cost model (COCOMO-II) is a commonly used method for estimating software cost. To further improve the accuracy of this model, researchers and practitioners have applied numerous computational intelligence algorithms to optimize their parameters. However, accuracy is still a big problem in this model to be addressed. In this paper, we proposed a biogeography-based optimization (BBO) method to optimize the current coefficients of COCOMO-II for better estimation of software project cost or effort. The experiments are conducted on two standard data sets: NASA-93 and Turkish Industry software projects. The performance of the proposed algorithm called BBO-COCOMO-II is evaluated by using performance indicators including the manhattan distance (MD) and the mean magnitude of relative error (MMRE). Simulation results reveal that the proposed algorithm obtained high accuracy and significant error minimization compared to original COCOMO-II, particle swarm optimization, genetic algorithm, flower pollination algorithm, and other various baseline cost estimation models.

Influence of the WRF Model and Atmospheric Reanalysis on the Offshore Wind Resource Potential and Cost Estimation: A Case Study for Rio De Janeiro State

The growing increase of offshore wind implementations has been made this modality subject to many resource assessment works. However, the variety of available wind datasets and the difficulty of the validation process over the ocean still leave uncertainty about the most relevant data for offshore wind resource assessment and how much they affect the energy estimation. This study investigates the influence of ERA5, CFSv2, MERRA2 atmospheric reanalyses, and the WRF model on the offshore wind resource potential and cost estimation of offshore wind projects. The coastline of the Rio de Janeiro state of Brazil is considered as the focus of the study region. Wind results are validated with in-situ measurements from three meteo-oceanographic buoys by the SIMCosta and PNBOIA programs, an anemometer placed on a floating vessel for production storage and offloading (FPSO), and seven weather stations positioned inland near the coastline from the National Meteorological Institute (INMET) of Brazil. Then, energy production and cost estimation are calculated employing the IEA-15 MW wind turbine, and the differences between the models are analyzed. The comparative analysis reveals significant differences in the annual energy production, mainly near to the coastline, impacting the cost estimation of the projects in the planning phase.

Critical Risks to Construction Cost Estimation

Abstract The prevalence of cost overrun in project delivery suggests an acute dearth of inclusive understanding of the effect of risks on construction cost estimation. In aberrant to the generic assumptions, customary to inquiries in construction risk researches, this paper appraised critical construction estimating risks. The study evaluated the sources, frequency and significance of construction estimating risks, using data from a questionnaire survey of 206 quantity surveyors in Nigeria. The data were analysed using factor analysis, Fussy Set Theory, Terrell Transformation Index (TTI), and Kruskal Wallis H tests. The results showed that estimating risks are correlate seven principal sources, namely: estimating resources, construction knowledge, design information, economic condition, the expertise of estimator, geographic factor, cost data, and project factors (λ, > 0.70 <1.0). Twenty-nine risk factors likewise emerged critical construction estimation risks (TTI, 69-87 > 65 percent) and the top three were low construction knowledge, inaccurate cost information and changes in government regulations (factor scores > 0.60 > 0.50). The awareness and accurate assessment of these risks into project cost estimation would reduce cost overrun. The study, therefore, recommends synergies between projects’ internal/ external environments for proper scoping of these risks into project estimates.

WITHDRAWN: Application of building information modelling (BIM) in managing the volume of high-rise building walls

Volume management is a mandatory task in the quality management of construction projects. This information lets the construction manager know how much of the work has been executed or progressed, and when the work has been completed. The speed and accuracy of volume management should be ensured, especially for projects with an urgent schedule or with overlapping tasks. However, the use of traditional 2D methods in the breakdown and management of high-rise building construction volume has recently faced quite a few challenges; for instance, some projects construction has been prolonged or project-cost estimates are unsound due to duplicated or omitted calculation. Therefore, this paper presents how Building Information Modeling (BIM) is used for breaking down and managing the volume in high-rise building walls. Using BIM, a new approach, compared to the traditional 2D drawings, helps the construction project managers to improve the construction quality, minimize errors in the acceptance and management of work volume, and accelerate and promote the project's success.

Improved Ensemble Feature Selection Based on DT for KPI Prediction

In the production process of large-scale machinery and complex industries, the key performance indicator (KPI) prediction is an essential part of project scheduling and cost estimation. The continuous enrichment of sensor types and functions brings us massive soft-sensing parameters for regression, but also brings severe challenges to algorithm learning. In this paper, an improved ensemble feature selection based on decision tree (EFS-DT) strategy for KPI prediction is developed. On the one hand, the ensemble of multi-criteria filtering results broadens the selector’s perspective without the time cost of superposition. On the other hand, credibility and similarity analysis are designed to eliminate the concerns of Dempster’s combination rule about conflict. After re-evaluating the variable scores, more high-quality variables can be selected to build a more accurate and robust KPI prediction model. Finally, a realistic shield tunnel case in China is used to evaluate the feasibility and effectiveness of proposed approach.

A Skills Measurement Framework For The Construction Industry: A Case Of Lusaka Province

Construction is characterised as being labour intensive and thus the continuous skills development will always be a matter of concern. The cyclic nature of the construction industry gives rise to skills mismatches which cause fluctuations in skills demand and supply. Investigating labour market conditions to assess skills demand and supply cycle of labour is a difficult task because of the scarcity or unavailability of aggregated local labour market information on the construction industry. This study investigated the issue of skills mismatches and developed a skills measurement framework to address it. The research design employed a mixed method approach to include both quantitative and qualitative data. A total of 162 firms consisting of 59 contractors, 80 consultants, 23 higher education institutions and 10 government institutions and local authorities in Lusaka Province whose core of business was inclined towards construction and development of transportation infrastructure were identified and used as the sample frame. Stratified random sampling was used to select respondents in the sample size. The method employed for data collection was the distribution of fifty (50) structured questionnaires and the conducting of three (03) semi- structured interviews with the aid of an interview guide. Quantitative data from the questionnaires was analysed using statistical software and qualitative analysis from interviews was analysed using content analysis. The most influential factors of supply and demand were; defined career paths, education and training requirements, salary scale and technological progression. Results revealed that the type of mismatches that existed were a skills gap and skills shortage. They revealed a shortage of Engineers and Construction Supervisors. Skills gaps were identified in the preparation of design specifications, estimation of project costs and preparing detailed cost plans, preparing construction method statements and technical specifications, as well technical and financial reporting. A sequential five-stage skills measurement framework structure was designed that investigated important aspects for skills development which are responsive to industry needs. The stages yield outputs which identify factors influencing skills demand and supply and skill competency requirements in construction, and assess the ability of the workforce meet the required skills competencies

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.

Contingency Management in Indian Construction Projects

The successful completion of Indian construction projects possesses an inherent uncertainty due to the nature of the construction industry along with prevailing tepid recovery of the Indian economy, which has made it difficult for construction companies to achieve required operational and financial performance. This necessitates having accurate project cost estimation and efficient contingency management to shield unknown project risk and address cost overrun problems. Therefore, this study aims to assess the current state, the issues faced in contingency management and evaluating the effectiveness of contingency in overcoming cost overrun problems in Indian construction companies. The study preferred a quantitative approach in capturing the required data by using structured questionnaires collected from 100 Indian construction companies (of contractors, vendors/suppliers, customers, and consultants) out of 335 companies with a response rate of 29.85%. This, along with archival data analysis of 50 Indian construction projects completed between Calendar Year (CY) 2014 to 2019. The captured data were analysed using simple statistical tools like Relative Importance Index (RII), mean score, standard deviation and ranking methods to ensure relevant results aligned with research objectives. The result findings of the study revealed that 60% of Indian construction companies preferred the traditional percentage method for contingency sum percentage addition to estimated project cost (a subjective approach based on the historical project performance) which may not be adequate in overcoming cost overrun problems. Archival data analysis found that contingency consideration was effective in accommodating cost overrun problems in 9 (18%) projects out of 50 projects. The study further found that contingency was utilised in an unplanned mode and having unclear contingency forecasting strategies. Based on result findings and observations, this study concludes that contingency should be estimated based on the scientific approach in place of a subjective approach to address project risks adequately, this along contingency consideration should be an integral part of the project risk assessment and should be effectively utilised during the execution stage.
 Keywords: Contingency, Cost Overruns, Indian Construction Projects, Operational and Financial Performance, Project Risk.

Project Monitoring and Evaluation and Project Success in Local Government in Rwanda

Efficiency of road infrastructure projects is essential for the economic growth and development of any country. These projects play a critical role in the economy in terms of wealth creation and provision of employment opportunities. Infrastructure covers a range of services, from public utilities such as power, telecommunications, water supply, sanitation and sewerage, solid waste collection and disposal, and piped gas; to public works such as roads, dams and canal works, railways, urban transport, ports, waterways and airports. Massive investments are put into infrastructure projects. Throughout the world, the business environment within which construction project operate continues to change rapidly for betterment of citizens, projects management are still failing to adapt and respond to the complexity of the new environment tend to experience survival problems. With increasing users of the road’ requirements, environmental awareness and limited resources and high competition, lack of skills of contractors have to be capable of continuously improving the efficiency of the project during implementation Efficient performance of road infrastructure projects is essential for economic growth and development of any country. Local construction firms contribute significantly towards realization of this goal. However, road projects experience challenges in completing within the budgeted cost, time schedule and attaining the desired quality. This research sought to establish the effects of project monitoring and evaluation on project success in local government road projects. The study carried out in the Kicukiro District on the Cricket road construction project in Rwanda, Gahanga Sector. The target population was 827, where the sample size used was 90 selected using probability sampling technique and the systematic sample method was used. The researcher used an explanatory research design to establish the causal relationship of the variables under study. Data were collected using questionnaire and analysed using SPSS version 22. The Statistical findings indicated that staff technical skills during the ME and not significant to meeting project costs compared to estimated project cost at p-value of 0.479>0.05, while not significant on helping the project to achieve its desired goals where the p-Value was 0.540>0.05. These statistics indicated that having technical skills does in M&E does not matter in project success. Possessing skills is not enough as using them properly, professionally and technically to enhance the project performance. The statistical findings indicated that the M&E budget is not significant on project completion on time as planned at p-value of 0.959>0.05 and not at the same time significant on meeting project costs as estimated at a coefficient of significance of p-value 0.745>0.05 and finally not significant on project meeting its set goals at p-value of 0.816>0.05. This shows that project performance is not only due to the available budget and approved, it depended on how the budget is effectively allocated to the task of monitoring and evaluation of the project as stipulated in the project design and implementation plan. It is clear from the regression analysis that stakeholder’s involvement in M&E helps the project management to collect feedback on what have been compared to the expected that satisfy the stakeholders needs. The findings indicate that, stakeholder’s engagement in M&E and feedback is significant on project competition on time compared to the expected time at a p-value of 0.01 0.05 while not significant also to project achieving its goals at p-value of 0.217>0.05. Due to lack of major influence during the project design and implementation, the feedback of stakeholders during the monitoring and evaluation may not have so much significant effect on the project success.

Risk Evaluation Model of Wind Energy Investment Projects Using Modified Fuzzy Group Decision-making and Monte Carlo Simulation

The recent increasing trend of investments in wind energy projects to support sustainable development requires an appropriate risk evaluation model to ensure the success of these projects. Early studies focus on opinion and discussion from subject matter experts. However, the expertise level in the subject is varied, and evaluation without considering expert competency can cause biased results. On the other hand, most of the project cost estimation models do not consider uncertainty in all cash flow parameters. In response, this article proposes a model that evaluates risks in wind energy investment projects by considering the knowledge and background of experts. Then, an integrated model of risk evaluation and cost estimation is developed. The model consists of three main stages: risk identification based on a systematic literature review (SLR); risk analysis phase 1 based on a modified fuzzy group decision-making; and risk analysis phase 2 based on a Monte Carlo simulation method. The main advantages of the proposed model are: (a) providing a comprehensive risk identification in wind energy investment projects; (b) using a modified fuzzy model to improve the risk assessment process by considering the expert competency in wind energy projects; and (c) establishing an integrated model to evaluate the cash flow of the investment. A wind farm in the Middle East is selected as the case study to examine the usability and practicality of the proposed model. The results show that the most important risks are ‘change in regulation and policy’, ‘dependency on the international market for importing raw materials’ and ‘market competitiveness’. On the other hand, the financial assessment under uncertainty shows that the profitability of the investment can be varied, and it emphasises the importance of an appropriate risk management process to guarantee the success of the investment.

A New Check System of Cost Estimation of Project Designs in Japan’s Local Governments

In Japan, “Pointing and Calling” is a known safety management method to prevent mistakes and ensure safety. This study constructed a new check system, “Verification Meeting/ Simple Verification Meeting,” that is based on the Pointing and Calling system and aims to prevent incorrect cost estimates for public works projects. It also contributes to improved staff morale and professional development. The new check system has five novel items compared with conventional cost estimation verification methods. The proposed system was put into actual use in Japan, during which time necessary improvements were made. As a result, there were no mistakes in cost estimation for about three years at the Port and Harbor Bureau of a certain city in Japan where the first author of this study worked. The system was further refined and could be adapted for use by all local governments, including those with few staff members.

Insights on Cost Estimation Methods and its Uses in Software Project Design

Cost estimation of software projects is risky task in project management field. It is a process of predicting the cost and effort required to develop a software applications. Several cost estimation models have been proposed over the last thirty to forty years. Many software companies track and analyse the current project by measuring the planed cost and estimate the accuracy. If the estimation is not proper then it leads to the failure of the project. One of the challenging tasks in project management is how to evaluate the different cost estimation and selecting the proper model for the current project. This paper summarizes the different cost estimation model and its techniques. It also provides the proper model selection for the different types of the projects.

Construction Cost Estimation Using a Case-Based Reasoning Hybrid Genetic Algorithm Based on Local Search Method

Estimates of project costs in the early stages of a construction project have a significant impact on the operator’s decision-making in essential matters, such as the site’s decision or the construction period. However, it is not easy to carry out the initial stage with confidence, because information such as design books and specifications is not available. In previous studies, case-based reasoning (CBR) is used to estimate initial construction costs, and genetic algorithms are used to calculate the weight of the retrieve phase in CBR’s process. However, it is difficult to draw a better solution than the current one, because existing genetic algorithms use random numbers. To overcome these limitations, we reflect correlation numbers in the genetic algorithms by using the method of local search. Then, we determine the weights using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using a hybrid genetic algorithm. Then, the model was verified with construction cost data that were not used for the development of the model. As a result, it was found that the hybrid genetic algorithm and case-based reasoning applied with the local search performed better than the existing solution. The detail mean error value was found to be 3.52%, 6.15%, and 0.33% higher for each case than the previous one.

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

Construction cost predictions to reduce time risk assessment are indispensable steps for process of decision-making of managers. Machine learning techniques need adequate dataset size to model and forecast the cost of projects. Therefore, this paper presents analysis and studied manuscripts that proposed for cost estimation with machine learning techniques for the last 30 years. The impact of this manuscript is deep studied of machine learning techniques and applied an analysis methodology in cost estimation based on direct cost and indirect cost of construction projects, which consists of two parts. In the first part, for study the proposals, we focus on collecting related studied from Google Scholar and Science Direct journals. The interested application areas for project cost estimation are building, highway, public, roadway, water-related constructions, road tunnel, railway, hydropower, power plant and power projects. The second part is regarded to the analysis of the proposals. For cost analysis, there are possibilities to consider two approaches as qualitative and quantitative. However, reflect to the machine learning techniques the quantitative approach is studied. In quantitative approach, we categorized the models in three parts, as statistical, analogues and analytical model and analyse them based on their features. Correspondingly, papers have been thoroughly investigated based on the application area, method applied, techniques implemented, journals, which have been published in, and the year of publication. The most important outcome of this study is to find out the different analytics methods and machine learning algorithms to predict the cost estimation of construction and related projects and aid to find out the suitable applied methods.

Correlation Model Between Estimated Project Cost and Net Present Value (NPV) on Transportation Infrastructure Projects Using Public Private Partnership (PPP) Scheme

Public Private Partnership (PPP) scheme has been used throughout the world as an effective tool to fill the gap between the surge in infrastructure development needs and the shrinking of public fiscal budget. The Ministry of Public Works and Housing 2030’s vision is focused on the construction of infrastructure. The state budget related to road infrastructure development around Rp 1.796 trillion but not all the needs fit to the budget. The aim of this research are identify field facts related to infrastructure financing with PPP, create correlation model and evaluate the suitability of the model. The success factor implementation of Public Private Partnership (PPP) scheme is the positive value of Net Present Value (NPV) that indicates the project is feasible. The correlation model between estimated project cost and Net Present Value (NPV) performed using statistical analysis is also used to make financial scheme from user-pay and government-pay on the PPP. The correlation model is Yt = 102.227.962 + 0,1098892t which the variable t can be changed into the number of estimation concession period. The result from this research are expected as a reference in determining project cost and negotiating the equity financial between user-pay and government-pay on PPP’s project.

Critical Factors Affecting Construction Price Index: An Integrated Fuzzy Logic and Analytical Hierarchy Process

Nowadays, many construction engineering and technology enterprises are evolving to find that prosperity is driven and inspired by an open economy with dynamic markets and fierce multifaceted competition. Besides brand and product uniqueness, the ability to quickly provide customers with quotes are matters of concern. Such a requirement for prompt cost estimation of construction investment projects with the use of a construction price index poses a significant challenge to contractors. This is because the nature of the construction industry is shaped by changes in domestic and foreign economic factors, socio-financial issues, and is under the influence of various micro and macro factors. This paper presents a fuzzy decision-making approach for calculating critical factors that affect the construction price index. A qualitative approach was implemented based on in-depth interviews of experts in the construction industry in Vietnam. A synthetic comparison matrix was calculated using Buckley approach. The CoA approach was applied to defuzzified the fuzzy weights of factors that affect the construction price index. The research results show that the top five critical factors affecting the construction price index in Vietnam are (1) consumer price index, (2) gross domestic product, (3) basic interest rate, (4) foreign exchange rate, and (5) total export and import.

Risk based initial cost assessment while planning a hydropower project

In the absence of perceived risks at planning stage, initial cost underestimation or overestimation is commonly existent in hydropower projects. This study suggests an expert fuzzy-TOPSIS multiple criteria decision-making (MCDM) methodology for the initial cost estimation of a hydropower project. Risk factors along with their likelihood and impact are analyzed in pre-feasibility stage to assess the cost intervals per MW capacity. Project developers can use this philosophy to screen projects and identify critical areas for detailed investigations. To demonstrate the methodology's effectiveness, a case study of 126 MW Myntdu Leshka hydropower project in Meghalaya, India, is adopted. This study contributes to the limited research available concerning the analysis of risk factors for the cost assessment of a hydropower project.

BUDGET ASSESSMENT FOR INVESTMENT-CONSTRUCTION AND DEVELOPMENT PROJECTS (THEORY AND PRACTICE)

The practical value of standards in the field of project management is often discussed by specialists in various fields of project activity. Practicing project managers at the lower and middle levels often complain about the lack of clear and effective tools for solving specific problems of the project management execution. However, the skillful standards application may increase the success likelihood of a wide range of diverse projects with obtaining the expected business values “at the output” of the project. The systematic experience of the best specialists, acquired during the implementation of various projects and placed in the standards, can become the basis for the professional competencies development and the formation of competitive advantages for project companies in various industries. The aim of this study is to establish links between the methods recommended by the Project Management Body of Knowledge (PMBoK) for determining the budget of projects and the methods used in practice for determining the cost of construction projects, formalized in the current regulatory documents and state building codes of Ukraine. To this end, the PMBoK standard determined the basic methodological foundations of the project cost management process, its components and characteristic features. Then, based on recent publications and building standards, the main stages of the life cycle of construction projects were established, in which the cost of design work is assessed, the goals of such an assessment and the project participants that carry it out are determined. As a result, a complete methodological identity was established between the project cost estimation methods recommended by the PMBoK standard and the construction cost determination methods specified in the current regulatory documents of the Ukrainian construction industry, which confirms the full suitability of the PMBoK standard for use in construction project management practice. Research findings determine the use of the standard by both novice specialists and qualified project managers of construction projects.

BIM for infrastructure: an efficient process to achieve 4D and 5D digital dimensions

Introduction of the Building Information Modeling (BIM) approach in Civil Engineering practices allows a design optimization through both an improved control of all the components of a project and an increased efficiency in the collaboration between all professionals involved. In Italy, major attention is being paid by scientists and professionals on this subject area, especially after the implementation of the national law DM 560/2017. This document will gradually rule the integration of BIM-based procedures for contractors in major public tenders. The present paper aims to propose a methodology for implementing an optimization dynamic model of time (4D) and costs (5D) across different project phases. To achieve this aim, the process relies on several authoring tools and 4D/5D modeling software, capable to incorporate a variety of project data in a unique and shared environment. The most effective methodology to maximize the interoperability between various codes will be presented. In this regard, a dynamic model involving an optimization-oriented approach for both the quantity surveying and the definition of the design management timeline is introduced. It is important to emphasize that these aspects are two of the major issues for the evaluation criteria in a civil engineering project. Also, this bidirectional process allows for a more accurate estimation of project costs as well as to achieve an optimized timeline of construction activities. Finally, it worth reminding that in case of constrained changes during the design phase, a dynamic model permits to effectively reiterate the process with a significant reduction of the overall computational time.