Predicting Project Cost Research Articles

A comparison of concrete quantities for highway bridge projects: preconstruction estimates vs onsite records

ABSTRACT This paper compares onsite concrete quantities to preconstruction estimates for 18 highway bridges in Canada to quantify the differences in quantities and to identify the driving factors. Material estimates completed during planning and design play a crucial role in predicting project cost, duration, and embodied CO2e emissions for construction projects. However, there is limited understanding of estimating material quantities for construction projects, and their impacts on other estimating processes, e.g., project cost, project schedule, embodied CO2e assessments. Results show that 3–87% greater concrete quantities are used onsite compared to estimates, with the bridges’ substructures responsible for most of the discrepancy. The findings of this study inform our understanding of the preconstruction estimates and their interpretation. Adjusting for the discrepancy between estimates and onsite measurements as well as targeting the drivers of unexpected material use has the potential to reduce environmental impacts, minimize cost overruns, and limit project delays.

Cost Performance for Residential Building Projects

The construction industry is one of the main sectors that play basic role in the urban and rural development of a society. It provides important ingredients for the development of an economy. However, many construction projects experience extensive cost overrun. This study is conducted to: (1) reveal the magnitude and direction of cost overrun in building construction projects implemented in the West Bank - Palestine over the years 2013 to 2018, (2) develop prediction models for predicting project cost overrun based on the contract price value, (3) identify the causes of cost overrun in building construction contracts in the West Bank – Palestine, (4) test the importance of cost overrun causes from consultants and contractors’ perspective. To achieve the study objectives, two methods are followed: (1) Field data collection for 102 building construction projects implemented in the West Bank during the period 2013 and 2018, (2) A questionnaire survey was conducted to identify and rank cost overrun causes from the perspectives of contractors and consultants. The findings reveal that100% of projects suffer from cost overrun with average of 34.58%, ranging from 15% to about 88%. The results show that cost overruns for building projects increased with contract prices. The questionnaire survey reveals that the top five cost overrun causes from combined view are: experience in contracts, insufficient time for estimate, incomplete drawings, materials’ price fluctuation, and political situation. It is hoped that the findings will guide efforts to improve the performance of construction industry in Palestine and other developing countries.

Using Earned Value Management with exponential smoothing technique to forecast project cost

Earned value management (EVM) is developed for cost management and is widely used to evaluate and forecast project cost. Through earned value management, we can calculate the deviation between cost and schedule, as well as performance indicators such as cost performance index (CPI), schedule performance index (SPI) and duration performance index (DPI), and then predict project cost, schedule and duration. However, it is often not comprehensive to rely only on EVM for project prediction, because the project in the next testing period is not necessarily invariable and similar to the previous activities, but dynamic and flexible, and has some correlation with the previous project activities. Therefore, the research method of this paper is to combine the well-known earned Management (EVM) with the mature exponential smoothing technique to simulate the actual data of 32 simulation projects and predict the final cost of the project. After comparing the prediction results with the results of tens of thousands of simulation experiments, the final research results show that the combination of cost performance index (CPI) from earned Management (EVM) and simple exponential smoothing technique have promising performance for the final cost estimation of the project.

Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects

The cost, time and scope of a construction project are key parameters for its success. Thus, predicting these indices is indispensable. Correct and accurate prediction of cost throughout the progress of a project gives project managers the chance to identify projects that need revision in their schedules in order to result in the maximum benefit. The aim of this study is to minimize the shortcomings of the Earned Value Management (EVM) method using an Artificial Neural Network (ANN) and multiple regression analysis in order to predict project cost indices more precisely. A total of 50 road construction projects in Fars Province, Iran, were selected for analysis in this research. An ANN model was used to predict the projects’ cost performance indices, thereby creating a more accurate symmetry between the predicted and actual cost by considering factors that influence project success. The input data of the ANN model were analysed in MATLAB software. A multiple regression model was also used as another analytical tool to validate the outcome of the ANN. The results showed that the ANN model resulted in a lower Mean Squared Error (MSE) and a greater correlation coefficient than both the traditional EVM model and the multiple regression model.

Predicting project cost overrun levels in bidding stage using ensemble learning

ABSTRACT Predicting project cost overruns in the bidding stage has undergone significant changes with the application of state-of-the-art techniques. Both modeling techniques and domain knowledge should be integrated to enhance predictions of cost performance. This study developed an ensemble-learning classification model to predict the expected cost-overrun levels of public projects and derive explanatory factors and key predictors. A database of 234 public-sector projects in South Korea was used, including project characteristics (i.e., project delivery method, project types, cost, and schedule) in combination with bidding characteristics (i.e., award method, number of bidders, bid to estimate ratio, number of joint ventures). The results yielded an average accuracy of 61.41% for five model runs. Furthermore, information on the project type being constructed is an important contributor to prediction accuracy. Results of the model enable project owners and managers to screen projects that are expected to incur excessive cost overruns and to anticipate budget loss during the bidding stage and before contracts are finalized.

Pre-Design Cost Modeling of Road Projects

The conceptual cost estimation is a critical indicator of the future of the project's success at the initial process before submitting the project design. Pre-Design cost measuring is recognized as the very important procedure which affects the future of constructing proposed projects of roads. Pre-design cost is a preliminary cost measuring and is considered the prediction of the cost of a project during the planning and design phase. This evaluating and measure provides a great foundation for the process of financial support decisions and cost control. The aim of this study is to create pre-design cost measuring model for constructing road projects by utilizing linear regression technique. The research methodology consists of the data collected from public sectors which are the actual cost of the constructed road projects in Erbil governorate, then models have been developed by applying SPSS software, then the models summarized and selected. The mean absolute percentage error (MAPE) has been calculated using Excel program to calculate degree of the accuracy for the produced models. The accuracy was between -29% and +51%. As a result of the study regression models are useful and has a big advantage in predicting project cost in pre-design process and the planning stage of the project, by using either simplex computer scheme or any measuring tools.

A New System to Evaluate Comprehensive Performance of Hard-Rock Tunnel Boring Machine Cutterheads

The accurate performance evaluation of a cutterhead is essential to improving cutterhead structure design and predicting project cost. Through extensive research, this paper evaluates the performance of a tunnel boring machine (TBM) cutterhead for cutting ability and slagging ability. This paper propose cutting efficiency, stability, and continuity of slagging as the evaluation indexes of comprehensive cutterhead performance. On the basis of research of true TBM engineering applications, this paper proposes a calculation method for each index. A slagging efficiency index with a ratio of the maximum difference between the slagging amount and average slagging is established. And a slagging stability index with a ratio of the maximum slagging fluctuation and average slagging is presented. Meanwhile, a cutting efficiency index by the weighed average value of multistage rock fragmentation of a cutter’s specific energy is established. The Robbins and China Railway Construction Corporation (CRCC) cutterheads are evaluated. The results show that under the same thrust and torque, the slagging stability of the CRCC scheme is worse, but the slagging continuity of the CRCC scheme is better. The cutting ability index shows that the CRCC cutterhead is more efficient.

Adequate Quantification of Project Cost Risks

Literature on project management contains an abundancy of references to abnormally high project cost overruns as well as bitter complaints that modern risk quantification methods could not accurately predict project cost outcomes in many cases. This article provides a) an explanation of abnormally high project cost overruns, b) a link of risk quantification with ‘project team's quality' (team's strengths and weaknesses) and bias, c) an explanation why all currently used risk quantification Monte Carlo methodologies are relevant to ‘strong teams' only, d) introduction of a new non-linear probabilistic (Monte Carlo) methodology to define adequate cost contingencies for projects managed by ‘weak teams', e) a practical case of the non-linear (Monte Carlo) probabilistic modeling, and f) rough calibration of non-linear probabilistic (Monte Carlo) models that could be practically used for rule-of-thumb estimating of project cost outcomes.

Reference Class Forecasting Method in Predicting Construction Project Duration

Project
 delay is a global problem affecting construction and other industries in many
 countries. Its impact on planning and budgeting can be serious for all
 stakeholders involved and difficult to resolve. The purpose of this study is to
 analyze the reliability of duration estimates of public building projects based
 on actual duration of similar projects carried out in the past.Turkey is used
 as a case study for this purpose and data from 643 public building projects
 completed in Turkey were collected. The data include contract durations, actual
 durations as well as the total construction areas for all projects. Reference
 Class Forecasting (RCF) method is proposed and used to investigate whether it
 would be possible to produce reliable and realistic project duration forecasts
 based on such data. RCF can realistically predict the actual final duration of
 the projects of different reference classes for various levels of acceptable
 risks. Original estimates of the contract durations in Turkey are generally
 optimistic or underestimated. Government buildings with the highest average
 construction area required lower uplift values on the estimated durations to
 produce accurate and realistic forecasts.So far the RCF method has been broadly
 applied to predict project cost rather than duration. This paper describes it
 use for forecasting duration in building projects.

Project Cost Escalation Prediction Models Based on System Dynamics Approach

Multiyear construction project is one of the riskiest project due to uncertainty and dynamic economic condition. In multi-years project, cost escalation is inevitable because during construction period various risk factors such as increasing of labor cost, materials cost, energy cost, and equipment cost will affect the costs of the projects. This paper proposes a model to estimate cost escalation based on System Dynamic (SD) approach. SD is used to represent correlation among risk factors influencing cost escalation and to calculate cost escalation during project period. Causal Loop Diagram (CLD) was made based on in-depth literature review and confirmed by the experts. To validate the model, 7 multi-years projects in Indonesia were used as case study. The result showed that the proposed model can accurately predict project cost escalation with an accuracy of 91.21%. Simulation of SD also showed that the change of scope of work, ambiguous contract condition, unpredictable conditions, project schedule delay, inflation, and extreme weather condition are the most influencing factor of cost escalation. Besides predicting cost escalation of project, this model can also be used as early warning system in multi-year projects environment.

Regression Modelling for Prediction of Construction Cost and Duration

Construction investments are sensitive to time and cost overruns. Delay and cost escalation are considered two threats to project success. The project objective is to develop a model to predict project cost and duration based on historical data of similar projects. Statistical regression models are developed using real data of building projects. The methodology is adopted in 3 steps: a) Data collection b) Statistical analysis using Statistical Package for Social Sciences (SPSS) software c) Interpretation of results. The real data of cost and duration of 51 building projects have been collected. In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modelling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. The analysis is done using SPSS developed by IBM Corporation. The Regression models have been developed using the data collected from Noel Builders, Kakkanad, Ernakulam to predict the project cost and duration. The developed models are validated using split sample approach. The model outputs can be used by project managers in the planning phase to validate the scheduled critical path time and project budget.

Predicting Project Cost Performance Level by Assessing Risk Factors of Building Construction in South Korea

There exists a strong relationship between the level of planning effort and overall project performance. Project stakeholders are concerned about various project objectives among which cost performance has long been regarded as one of the most crucial. Recognizing the lack of a systematic approach in assessing building project risk in association with cost performance prediction, this study identified 49 risk factors from various sources and quantified the relative impact of each factor. By normalizing these values, a Project Risk Score (PRS) has been developed for the purpose of predicting cost performance on a particular project. The 13 real case studies showed a statistically significant relationship between the PRS and cost performance. Predicting cost performance is not simple. Project managers must consider not only factors that are innate to the project, but also project-independent circumstances. This study provides straightforward guidance in assessing a particular project in order to recognize the level of cost performance before project execution. This risk-based performance prediction can improve early-stage project evaluation and enhances the possibility of achieving a sustainable business in a challenging environment.

Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models

It is commonly perceived that how well the planning is performed during the early stage will have significant impact on final project outcome. This paper outlines the development of artificial neural networks ensemble and support vector machines classification models to predict project cost and schedule success, using status of early planning as the model inputs. Through industry survey, early planning and project performance information from a total of 92 building projects is collected. The results show that early planning status can be effectively used to predict project success and the proposed artificial intelligence models produce satisfactory prediction results.

Financial Pricing of Software Development Risk Factors

The ability to price (monetize) software development risks can benefit various aspects of software development. Cost estimators predict project cost by adjusting a project's nominal cost on the basis of risk factors' (cost drivers') expected values, but the predicted cost is often inaccurate because risk factors' actual values normally deviate from expectations. Because variability is a widely used risk measure in finance, this risk-pricing method relates risk factor variability to project cost variability. The method estimates two parameters for each risk factor: extra cost incurred per unit exposure and project sensitivity. Several areas can benefit from the benchmark risk-pricing parameters obtained when applying this method with a cost estimator such as Cocomo.