Construction Project Cost Research Articles

Project Construction Cost Control under the Mode of Bill of Quantities

Under the bill of quantities pricing model, the price of the contract is determined in advance, making the focus of enterprise project cost management be to control the expenditure of construction costs on the premise of established revenue, reduce the risks brought about by the bill of quantities pricing model, strengthen cost control, give play to the advantages of strategic cost management, and ultimately minimize project risks to maximize the benefits obtained by the enterprise. This paper mainly analyzes the problems existing in the cost control and cost management process of construction enterprises under the bill of quantities pricing mode, and proposes corresponding solutions based on the existing problems, hoping to increase the profits of enterprises from cost control and improve their market competitiveness.

Hybrid fuzzy arithmetic-based model for determining contingency reserve

Calculating an accurate contingency reserve amount is vital to managing construction project costs and risks. Current methods for determining contingency have limitations in incorporating risk response actions (RRAs), accounting for insufficient quantitative historical data, considering time-dependent variables, and capturing cause-and-effect interactions between risk events (REs) together and with RRAs. This paper presents a hybrid fuzzy arithmetic-based contingency reserve model (HFACRM) that addresses these limitations by 1) considering time-dependent probability and impact values of risk events and RRAs, 2) modeling causal relationships among variables affecting contingency, and 3) addressing subjective uncertainty of linguistic evaluations in risk management. HFACRM considers more realistic complexities and characteristics of projects compared to other models, resulting in a significant improvement in the accuracy and effectiveness of determining contingency reserve. A wind farm power generation project was used as a case study to demonstrate and validate HFACRM.

Assessing the COVID-19 Impact of Projects under Construction with Monte Carlo Simulation

The outbreak of the novel coronavirus pneumonia (COVID-19) in 2019 and the 2022 war in Ukraine have had profound global impacts on travel and logistics, disrupted the material supply chain, significantly influenced the cost and progress of construction projects, and further impacted the operational effectiveness of firms. Despite some existing studies providing valuable insights into the impact of COVID-19 on the construction industry, there remain research gaps that need to be addressed. Prior studies have mainly focused on the immediate impact factors of the pandemic, such as supply chain disruptions and workforce shortages, and strategies for effectively reducing or eliminating these risks. However, there is a need for research that delves into the long-term implications of these disruptions. So far, no relevant research has quantified the broader impact of the epidemic. Thus, this study aims to analyze the effects of the pandemic and the war on 136 construction industry professionals, their projects, and firms through literature review, questionnaire surveys, and expert interviews. The study compiles a list of significant risk factors for construction projects between 2019–2022, including their probability of occurrence, impact over time, and overall cost. The study also analyzes and discusses the impact of these high-risk factors as of 2022. To quantify the impact, cost, and level of exposure to these risks suffered by actual construction projects over this period, the Monte Carlo simulation method is introduced. This approach provides contractors with early prediction of risks and appropriate responses to mitigate risks.

Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: a case study from Jordan

Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: a case study from Jordan

Construction schedule and cost risk for large and small light water reactors

Capital cost and construction schedule estimates for nuclear projects often under-estimate the realized costs upon project completion. Uncertainty quantification of nuclear construction project cost estimates have seldom been used in open-literature and have not shown to effectively bound the realized schedules and costs. Building on recent studies of advanced light water reactor costs and construction schedules, this paper explored the risk of different construction delay drivers and the implication on total installed cost. Using data from previous nuclear projects, as well as other megaprojects and analyses, we modeled the impact of supply chain delays, human error, change orders, and productivity on four different reactor architectures: a large passively safe PWR, a large modular BWR, small modular BWR, and a multi-module PWR. The probability and impact of supply chain delays and change orders were modeled based on the AP1000 construction experience at Vogtle Units 3 & 4, while the human error and productivity models came from non-nuclear sources. The estimated risk to cost overrun and schedule delays were found to be significant. The multi-module plant was the most susceptible to construction delays, and the small modular BWR was the least susceptible. However, the risk mitigation by a small reactor only offset the lost economy of scale in a few scenarios relative to the large reactors, because there was still significant onsite work despite modularization. Access to large labor markets mitigated the upper bound cost and schedule risks significantly for all reactor types. Finally, design changes and the resulting productivity hits were the largest driver of construction delays, and human error was the second largest driver, but supply chain delays only accounted for a small fraction of the delays for the assessed water-cooled concepts.

Construction Time-Cost Optimization Modeling Using Ant Colony Optimization

In the field of construction project management, time and cost are the most important factors to be considered in planning every project, and their relationship is complex. The total cost for each project is the sum of the direct and indirect cost. Direct cost commonly represents labor, materials, equipment, etc.Indirect cost generally represents overhead cost such as supervision, administration, consultants, and interests. Direct cost grows at an increasing rate as the project time is reduced from its original planned time. However, indirect cost continues for the life of the project and any reduction in project time means a reduction in indirect cost. Therefore, there is a trade-off between the time and cost for completing construction activities.In this research, modeling of time-cost optimization, generating global optimum solution for time and cost problem, and lowering construction time and cost using ant colony optimization algorithm

Use of Risk Analysis Tools in Investment Construction Project Cost Management

The paper examines the possibilities of improving the accuracy of estimating the value of an investment and construction project based on various analysis and evaluation tools. Accurate estimates can be achieved using the Project Value Model at all stages of the project lifecycle. The stages of estimating the cost of an investment and construction project are considered, including costing, i.e. determining the costs of individual works depending on the calculation of the volume of work and pricing — determining the total cost of the project taking into account cost, profit and contingency provisions. The work carried out a comparative analysis of the methods of estimating the cost of the project: methods of high-level assessment, analog and method of parametric estimates, detailed assessment of the project costs, the use of which should be carried out iteratively. It is noted that risk management tools should be considered in combination with the project cost assessment due to the dependence of the goal in identifying and assessing risks with maximum performance and lowest cost. The article studied various approaches to assessment, in particular: estimating the cost of the project at the pre-investment stage in the form of a rough (approximate) value; estimate the project cost at the business planning stage as the estimated value of the cost justification Project Cost Estimation in the Process of Costing by Activity Type — Budget Cost Assessment Estimate the final planned project cost to be included in the project budget — accurately estimate the project cost. The article proposes the use of various tools and methods of risk analysis at the stages of the life cycle of an investment and construction project.

Developing a Model to Estimate Cost of Construction Projects based on Target Costing Approach

Developing a Model to Estimate Cost of Construction Projects based on Target Costing Approach

A Review on Implementing Lean Construction Technique in Construction Industry

In this literature review the concept of lean construction and its impact on time, cost, and resource management in construction projects are explored. The study examines various research articles and academic papers in the field of lean construction to identify the primary themes and findings. The review indicates that lean construction practices can have a positive effect on time, cost, and resource management by reducing waste, enhancing communication, and encouraging collaboration among stakeholders. The implementation of lean construction principles has been demonstrated to lead to shorter project durations, lower costs, and better resource allocation. However, the review also acknowledges the challenges associated with implementing lean construction in the construction industry, such as resistance to change, limited knowledge and understanding, and difficulties in assessing its impacts. In conclusion, this literature review concludes that lean construction has the potential to enhance time, cost, and resource management in construction projects, and proposes the need for further research to overcome the challenges and facilitate its adoption.

Factors Affecting Cost and Time Overrun In Construction Project

Factors Affecting Cost and Time Overrun In Construction Project

Price Adjustment Practices and Their Impacts on Bridge Construction Contracts

This paper assesses the patterns and trends of price fluctuations and price adjustment practices in RCC T-beam and prestressed bridge construction contracts. Using a questionnaire survey, we conducted a descriptive research to document the existing practices. The perceptions of clients and contractors were measured using mean score and standard deviation on issue, followed by a consistency check. We then analysed the major components of price adjustments to identify trends. Price adjustment provisions are planned during the procurement planning and bid preparation stages of procurement cycle, and are used as necessary during the contract implementation stage. These provisions are meant to give protection to the contractor against price escalation. In the absence of any compensation provision in bridge construction contracts, price fluctuation can affect the time, cost and quality parameters of construction projects. This may lead to delays or cancellation of projects, reduced number of bidders, poor quality, problems with cash flow, and loss of interest of stakeholders in the project. The research draws attention to the serious issues of price adjustments among bridge projects to ensure constructability. This is the first attempt to address the issue in bridge contracts of Nepal and can serve as a starting point for further research on this topic

Systematic Review of the Time-Cost Optimization Models in Construction Management

The construction sector is a crucial contributor to the national and global economy. Therefore, improving the efficiency and effectiveness of construction projects can have a significant impact on gross domestic product (GDP). However, managing construction projects can be challenging due to the uncertainties and complexities involved. The three primary interrelated constraints of construction projects, namely, time, scope, and cost, require effective management to ensure successful completion. To optimize the time and cost of construction projects, various optimization models and techniques have been proposed in the literature. This paper presents a systematic review of the time-cost optimization models in construction management and proposes some future work to improve the solution of the considered problem. The review categorizes the existing models into three categories: exact models, approximate models, and hybrid algorithm models. The exact models provide optimal solutions but require a lot of computational time and may not be efficient in solving multi-objective and large-scale problems. The approximate models provide near-optimal solutions and reduce computational effort but may not be efficient in solving large-scale projects. The hybrid algorithm models combine the good properties of different algorithms to provide high-quality and efficient solutions. The purpose of this paper is accomplished through a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The paper analyzes the contribution, advantages, and limitations of each category and provides recommendations for future work. Based on the review, several recommendations for future work are provided, including the development of hybrid models that combine different optimization techniques, the incorporation of risk management into optimization models, and the use of advanced data analytics techniques to improve the accuracy of optimization models. Overall, this paper provides an up-to-date comprehensive review of the time-cost optimization models used in construction management and offers valuable insights for researchers and practitioners in this field. The findings of this review can be used to guide future research and improve the effectiveness of optimization models for construction projects.

Estimating the volume of civil construction materials by machine learning models

Previous construction project cost models, especially in the project initiation phase, often mainly focused on estimating the total project cost without taking into account their constituent aspects such as materials, labor, machineries and equipment. Therefore, building a material quantity estimation model will have a positive impact on improving the accuracy of the total project cost. There have been many studies related to this issue, but there are few studies on building a model to estimate the quantity of materials for civil projects with reinforced concrete structures and they use specialized software (which is difficult to access for many subjects in the construction industry). The founding of many machines learning software, especially Weka software, helps to model with powerful algorithms with high reliability. In this study, suitable machine learning models will be proposed for estimating the quantity of materials as: concrete, formwork, steel of the components: foundation, column, beam and floor. Suitable machine learning models will be suggested to rank for each different model.

Dynamic-Bayesian-Network-Based Project Cost Overrun Prediction Model

One common problem in the construction industry is project cost overrun. Cost overrun can have significant impacts on financial profitability, project completion, project quality, and stakeholder satisfaction. The average percentage of construction project overrun can vary widely depending on the project type, size, complexity, and location. Many approaches can be adopted to prevent or mitigate project cost overrun, and one of them is a more accurate cost estimate and prediction. Several studies on the construction project cost overrun estimation and prediction have been conducted based on historical data; nevertheless, each project has its project characteristics and cost trend. Real-time, project-specific cost data are more reliable for forecasting the cost trend of the project itself. There are many influence factors that may interdependently affect the construction project cost overrun. This paper proposes a real-time predict cost overrun risk prediction model based on the influence factors and their interdependence as well as the corrective actions if adopted. This study used a dynamic Bayesian network (DBN) to formulate problem architecture and to use the input–output hidden Markov method (I/O HMM) with particle filter (PF) to run inference. Six building and mass rapid transit projects in Taiwan were used as model validation and comparison. The posterior probabilities from the DBN-based cost overrun risk prediction model were highly consistent with the cost overrun ratios of real construction projects. Moreover, it is superior to other prediction models in terms of accuracy. The proposed model could provide project managers with an early alert for cost overrun.

Project Cost Overrun Risk Prediction Using Hidden Markov Chain Analysis

Construction project cost overrun is a common problem in the construction industry. The cost of construction projects is thought to have increased by approximately 33% on average. Several types of research on construction project cost overrun have been conducted and these generally rely on historical data. However, whilst each project has its own project characteristics and cost trend, real-time project cost data are more reliable to forecast its own cost trend. This paper proposes a real-time hidden Markov chain (HMM) model to predict cost overrun risk based on project-owned cost performance data and the corrective actions if adopted. The cost overrun events occurrence in this model was assumed to follow a Poisson arrival pattern. Real-time HMM with a particle filter was used to run the simulation. One SRC building project in Taiwan was used for model validation and comparison. The posterior probabilities from the real-time HMM model were highly consistent with the cost overrun ratios of real construction projects. The proposed cost overrun prediction model could provide an early alert of cost overruns to the project manager. Based on the survey of cost overrun risk and significantly influential factors, we propose effective cost management plans to alleviate the frequency of project cost overrun.

Assessment of suppliers’ collaboration on construction project in Ebonyi state

In Nigeria, the concept of suppliers’ collaboration is still a new area to construction stakeholders. The construction companies in Ebonyi State are faced with enormous challenges which involve the integration of all construction processes and activities of different agents and specialists hired by clients for successful project delivery. To address the problem of construction project in Ebonyi State, construction stakeholders require collaboration which suppliers’ collaboration is the most pertinent. The aim is to assess the factors affecting contractors–suppliers relationship on successful construction project delivery in Ebonyi State. The general objective is to identify and determine the factors affecting contractors–suppliers relationship in construction industry and impact of supplier’s collaboration on the cost of construction project in Ebonyi State. The research sought to ensure at least 95% level of confidence with a total population of 400 and sample size set to 250. Primary and secondary sources were used to generate data with the aid of structured questionnaire and oral interviews directed to both private firms and public firms in construction industry. In total, 250 forms containing a set of question were submitted randomly to selected medium and large-sized construction firms in Ebonyi State. Data collected were presented in tables containing frequencies of the responses and their corresponding percentages. Frequency distribution, relative importance index (R11), Chi-square (X2), Python spyder IDE, statistical package for social science students(SPSS) and Pearson product moment correlation coefficient were the analytical tools used in the study. The result of the analyses revealed that, correlation matrix used to measure the relationship between contractors and supplier variables. The matrix depicts there is a linear correlation between all possible pairs about contractors and suppliers. There exist a relationship between contractors and suppliers as shown with other pairs in the correlation matrix that connects to objective number 3 and addresses the research question number 3. This implies that as the contractor-supplier approach short term or/and long term increases, the supplier collaboration on the cost of construction project in the Ebonyi State is expected to increase. The study concludes that the main drivers of the success of supplier collaboration appear to be the elements of trust and teamwork. It is these which ensure the continued relationship and ensure that both parties derive the benefits necessary for the partnership to continue and therefore; made the following recommendation. A detailed schedule of materials supply should be provided by the contractor. The schedule should contain the time required for material to arrive on site. Thus, results in reducing unproductive time on site and cost saving.

COMPARATIVE ANALYSIS OF DIRECT WASTE USING SOFTWARE CUTTING OPTIMIZATION PRO AND THE COST OF USING CONVENTIONAL REINFORCEMENT AND WIREMESH IN FLOOR SLAB WORK. (CASE STUDY: OFFICE AND DEPOT CONSTRUCTION PROJECT OF PT. GUDANG GARAM)

Rebar is one of the main materials in a building. The material has been widely innovated by product providers. In addition to using conventional reinforcement on the floor slab, it is now possible to replace the reinforcement with wire mesh reinforcement. Because material costs are a very large cost in construction projects, it is necessary to do proper material planning. This final project will compare the two reinforcements with the aim that project implementers can plan materials appropriately so that it is expected to minimize the huge cost expenditure. In this final project, the reinforcement in the floor slab work will be reviewed in terms of direct waste reinforcement which will be assisted by software cutting optimization pro and reinforcement costs using the AHSP stipulated by Permen PUPR No.1 of 2022. After various stages are carried out, a comparison of the direct waste level, direct waste cost, and cost of the two reinforcements will be obtained. A Comparison of the three reviews found that wire mesh reinforcement is more efficient than using conventional reinforcement.

Multivariate regression approaches to predict the flexural performance of cellulose fibre reinforced extruded earth bricks for sustainable buildings

Regression analysis is commonly used to predict the compressive strength of soil-based materials to reduce the time and cost of construction projects. This study presents the results of multivariate regression analysis on the prediction of modulus of rupture and specific energy of cement-stabilised earth bricks reinforced with bamboo cellulose fibres as a function of cellulose fibre percentage, curing temperature and curing time. Statistical modelling is carried out on the experimental data of the four-point bending test of clay brick stabilised with 10 wt% Ordinary Portland Cement reinforced with 0, 5, 7.5 and 10 wt% organosolv bamboo pulp fibres. The water content of the unreinforced specimen was adopted at 25 wt% of the solid materials based on the plastic limit value of the soil, while the value of the water content of the samples with cellulose fibres was 35 wt% due to the affinity of the cellulose with water as well as to allow good extrudability of the pulp. The bricks were cured under different conditions (23 °C, 60% RH and 60 °C, 100% RH) and at different ages (14 and 28 days). Regression analysis and two-way ANOVA were studied to assess the accuracy, correlation and effect of each variable on the prediction of the random response variable. The results showed that non-linear regression analysis provides the best-fitting statistical model with an accuracy of 96%. In addition, the curing temperature and the percentage of cellulose fibres significantly affect the bricks bending performance, while the effect of curing time is the least visible. This non-linear model can be adopted as a suitable model to predict the flexural properties of cellulose pulp fibre-reinforced earth bricks for a sustainable building solution in developing countries. For better generalization and practical application of this method to predict the flexural performance of cellulose pulp fibre-reinforced earth bricks, a large data set of a broader range should be explored.

Construction cost prediction based on adaptive boosting and artificial neural networks

The artificial bee colony algorithm and multilayer error back-propagation neural networks commonly used in construction project cost forecasting suffer from slow training speeds and high costs. A combination of the beetle antennae search, support vector machines, adaptive boosting and genetic algorithms was proposed to solve these problems. Support vector machine optimisation was accomplished using the beetle antennae search algorithm. The enhanced genetic algorithm was then used directly to swap out the fit solutions for the unfit ones. One hundred projects completed during the last 3 years were chosen from a network integration database to serve as the training data set after developing the prediction model. Using actual cost information and trial and error, appropriate parameters were chosen, and combinations of algorithms were selected for comparison. The maximum relative error of the improved method was 9.01%, which was 34.68% lower than that of the baseline method, while the smallest relative error was 0.59%, which was 1.58% lower than that of the baseline method. The ’innovation of the study lies in the addition of the beetle antennae search algorithm and enhancement of the genetic algorithm. The former significantly increased the search efficiency of the network, while the latter generally increased the population fitness and mitigated the drawback of the genetic algorithm which was prone to local convergence.

The Structure Design Optimization of Transmission Line Pole Tower Body

Tower as an important part of transmission line project construction, in the overall project construction cost accounts for more than 30%, to ensure the quality of project construction, improve the level of structural optimization, has a positive impact on the stable operation of the power system. Therefore, while gradually expanding the scope of electric power investment and construction, China has strengthened the research on the structure optimization design of transmission line tower body, effectively controlled the project cost and improved the quality of construction and installation in the comparative analysis. Based on the understanding of transmission line pole tower design theory, according to the current common foundation selection and specific requirements, this paper deeply explores the transmission line steel pole tower body operation platform, and carries on the verification analysis to the practice optimization design scheme. The final results show that the optimized design of the tower structure can better ensure the stability and security of power grid system construction and operation.