• Geomechanics and Engineering
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• v.28 no.1
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• pp.65-75
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• 2022

Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.22-34
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• 2023

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1299-1306
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• 2014

Recently, there are many roundabouts installation works which are ordered to provide convenient transportation to road users as well as to eliminate traffic accidents and traffic delays. This study propose an approximate construction cost estimation model for early stages of roundabout construction. The model is designed considering the conditions of the early stage roundabout construction sites in Jeju. The regression equation of approximate construction cost was derived through regression analysis of 25 design data of roundabout construction in Jeju, and it was analyzed to have a high prediction accuracy. Finally, results verifies high prediction accuracy of the derived regression equation. Difference between the estimation cost and the design cost was only 2.3%, 3.7%, and 5.8% that verifies the high accuracy of the proposed approximate construction cost estimation model.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.892-896
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• 2005

This paper collected 132 schools reconstruction projects in central Taiwan, which received the most serious damage from the Chi-Chi Earthquake. Regression analysis was implemented to build the prediction model of the cost and the duration for the collected projects. It is found that the cubic regression models are capable for predicting the cost and the duration of the projects contracted by the central agency of which the contracting awarding approach was based on the most advantageous tendering (MAT) approach. On the other hand, power regression models are capable for predicting the cost and the duration of the projects contracted through the low bid tendering (LBT) approach. It is also found that the performance of the regression prediction model differs in accordance with organizations that contracted the reconstruction projects.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.495-506
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• 2021

A Conceptual cost estimate, which is computed in the preliminary step of a project, is important for decision-making by a contractor in terms of the project budget, economic feasibility and validity analysis, and alternative comparisons. Therefore, a high error rate of a prediction model for a conceptual cost estimate can lead to various problems including excessive project expenditures and a delayed break-even point. this study proposed optimal impact factors by configuring quantitative impact factors computable in a preliminary step in various cases(combinations of impact factors). subsequently, the accuracy of different cases was comparatively analyzed by using the cases as input values of a prediction model using regression analysis. when the optimal combination of impact factors proposed in this study and other combination of impact factors were applied to the prediction model, the regression analysis-based prediction model exhibited 0.2-4.7% improvements in accuracy, respectively. the optimal combination of impact factors proposed in this study improved the accuracy of the prediction model of a conceptual cost estimate by removing unnecessary impact factor.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1474-1480
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• 2013

This study attempted to estimate construction costs in accordance with the resource-based cost estimation (unit cost price) system by diameter for TBM method, and analyzed the direct cost and the total cost. Based on such figures, this study performed a regression analysis and proposed a model for an equation for estimating construction costs. model for the resource-based cost estimation (unit cost price) system classified by diameter for TBM method proposed by this study can be effectively applied to business planning, preliminary investigation, feasibility study, construction cost estimations in the early design stages.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.497-501
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• 2013

An important decision-making element for the success of housing redevelopment projects is a prediction of the profitability of redevelopment. Risk factors influencing profitability were deduced through a review of the literature about profitability and a risk analysis developed by a survey of maintenance projects. In addition, a profitability prediction depending on the analysis of risk factors is necessary to judge the business feasibility of a project in the planning stages. A profitability prediction model of management and disposal method, which is calculated by proportional rate and which helps estimate contributions to profitability, is proposed to prevent difficulties in business development. The proposed model has the potential to prevent interruptions, reduce the length of projects, generate cost savings, and enable rational decision-making during the project period by allowing a judgment of profitability at the planning stage.

• Korean Journal of Construction Engineering and Management
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• v.8 no.5
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• pp.191-200
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• 2007

This paper introduces a tool for predicting potential cost overrun during project execution and for quantifying the uncertainty on the expected project cost, which is occasionally changed by the unknown effects resulted from project's complications and unforeseen environments. The model proposed in this stuff is useful in diagnosing cost performance as a project progresses and in monitoring the changes of the uncertainty as indicators for a warning signal. This model is intended for the use by project managers who forecast the change of the uncertainty and its magnitude. The paper presents a mathematical approach for modifying the costs of incomplete work packages and project cost, and quantifying reduced uncertainties at a consistent confidence level as actual cost information of an ongoing project is obtained. Furthermore, this approach addresses the effects of actual informed data of completed work packages on the re-estimates of incomplete work packages and describes the impacts on the variation of the uncertainty for the expected project cost incorporating Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem. For the illustration purpose, the Introduced model has employed an example construction project. The results are analyzed to demonstrate the use of the model and illustrate its capabilities.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.702-703
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• 2015

Currently, according to the climate change, serious damage by Typhoon has been occurred in the world. In this respect, the research on the damage prediction model to minimize the damage from various natural disaster has been conducted in several developed countries. In the case of U.S, various damage prediction models of buildings from natural disasters have been used widely in many organizations such as insurance companies and governments. In South Korea, although studies regarding damage prediction model of hurricane have been conducted, the scope has been only limited to consider the property of hurricane. However, it is necessary to consider various factors such as socio-economic, physical, geographical, and built environmental factors to predict the damages. Therefore, to address this issue, correlation analysis is conducted between various variables based on the data of hurricane from 2003 to 2012. The findings of this study can be utilized to develop for predicting the damage of hurricane on buildings.