Programmatic Cost Risk Analysis for Highway Megaprojects

Highway megaprojects (construction projects over $100 million) are fraught with uncertainty. These projects have historically experienced increases in project costs from the time that a project is first proposed or programmed until the time that they are completed. Persistent cost underestimation reflects poorly on the industry in general but more specifically on engineers. Traditional methods take a deterministic, conservative approach to project cost estimating and then add a contingency factor that varies depending on the stage of project definition, experience, and other factors. This approach falls short, and no industry standard stochastic estimating practice is currently available. This paper presents a methodology developed by the Washington State Department of Transportation (WSDOT) for its Cost Estimating Validation Process. Nine case studies, with a mean cumulative value of over $22 billion, are presented and analyzed. Programmatic risks are summarized as economic, environmental, third party, right-of-way, program management, geotechnical, design process, construction, and other minor risks. WSDOT is successfully using the range cost output from this procedure to convey project costs to management and the public.     

Military Facility Cost Estimation System Using Case-Based Reasoning in Korea

Numerous cost estimations are made repetitively in the initial stages of construction projects in response to ongoing scope changes and often need to be recalculated frequently. In practice, the square foot method, considered an effective method for time-saving, is widely used. However, this method requires a great amount of effort to calculate a unit price and does not consider the uniqueness of each case. Thus, the use of the square foot method could bring about unwanted consequences. For example, in the case of military projects in Korea, significant differences have been reported between estimations made using this method and the actual costs. In an effort to deal with this challenging issue, this research develops a military facility cost estimation (MilFaCE) system, based on case-based reasoning (CBR), using case data from 422 construction projects at 16 military facilities. Based on system validation experiments involving 10 military officers (engineers), the effectiveness of the system in terms of estimation accuracy and user-friendliness is confirmed. Consequently, this research can be a CBR application example of construction cost estimation and a basis for further research into the development of cost estimate systems.     

Defining Triangular Probability Distributions from Historical Cost Data

During the development of an automated cost estimating system, several factors led to the selection of the triangular probability-density function to model historical construction costs. The triangular-density function is customarily used when function parameters are directly estimated by experts. A typical example is for estimating activity durations by identifying a minimum value, a most likely value, and a maximum value. These values are then used to construct triangular-density functions to represent uncertain activity durations. For this work, however, it was necessary to estimate parameters of the triangular-density function using historical cost data. A methodology was developed to generate test data and compare three methods of parameter estimation—maximum likelihood, moment matching, and least-squares curve-fitting techniques. It is concluded that optimized moment matching and least-squares techniques produce more accurate parameter estimates, while maximum likelihood estimation yields less accurate results. It is further concluded that the least-squares minimization method always performed as well as or better than the optimized moment matching technique and was therefore selected as the method of choice for the project.     

Multiple Simulation Analysis for Probabilistic Cost and Schedule Integration

One of the major goals of the construction industry today is the quantification and minimization of the risk associated with construction engineering performance. When specifically considering the planning of construction projects, one way to control risk is through the development of reliable project cost estimates and schedules. Two techniques available for achieving this goal are range estimating and probabilistic scheduling. This paper looks at the integration of these techniques as a means of further controlling the risk inherent in the undertaking of construction projects. Least-squares linear regression is first considered as a means of relating the data obtained from the application of these techniques. However, because of various limitations, the application of linear regression was not considered the most appropriate means of relating the results of range estimating and probabilistic scheduling. Integration of these techniques was, therefore, achieved through the development of a new procedure called the multiple simulation analysis technique. This new procedure combines the results of range estimating and probabilistic scheduling in order to quantify the relationship existing between them. Having the ability to accurately quantify this relationship enables the selection of high percentile level values for the project cost estimate and schedule simultaneously.     

Cost and Schedule Monitoring of Industrial Building Projects: Case Study

Performance measurement is a helpful tool for taking corrective actions and controlling a project as far as this enables accurate time and cost forecasts during the first stages of the construction effort when the management team still has opportunities to make adjustments. This paper overcomes the dilemma of practicability and predictability of traditional estimates at completion based on early progress measurement by presenting the empirical results from the construction project of an industrial facility. The case may be a reference practice for assessing time and cost performance measurement of any building, whose layout can be reasonably partitioned into repeatable portions. In such circumstances, an effectively-managed traditional earned value method and appropriate metrics for computing performance provide project managers with accurate forecasts as useful tools for successful project management and control.     

Hybrid Models of Neural Networks and Genetic Algorithms for Predicting Preliminary Cost Estimates

This technical note applies hybrid models of neural networks (NN) and genetic algorithms (GA) to cost estimation of residential buildings to predict preliminary cost estimates. Data used in the study are for residential buildings constructed from 1997 to 2000 in Seoul, Korea. These are used in training each model and evaluating its performance. The models applied were Model I, which determines each parameter of a back-propagation network by a trial-and-error process; Model II, which determines each parameter of a back-propagation network by GAs; and Model III, which trains weights of NNs using genetic algorithms. The research revealed that optimizing each parameter of back-propagation networks using GAs is most effective in estimating the preliminary costs of residential buildings. Therefore, GAs may help estimators overcome the problem of the lack of adequate rules for determining the parameters of NNs.     

工程项目管理中的成本管理

工程项目成本管理是为保障项目实际发生的成本不超过项目预算而开展的项目资源计划、项目成本估算、项目预算编制和项目预算控制等方面的管理活动。是保证施工企业正常经营的重要的基础管理工作。     

Preliminary Cost Estimation Model Using Case-Based Reasoning and Genetic Algorithms

This study proposes a preliminary cost estimation model using case-based reasoning (CBR) and genetic algorithm (GA). In measuring similarity and retrieving similar cases from a case base for minimum prediction error, it is a key process in determining the factors with the greatest weight among the attributes of cases in the case base. Previous approaches using experience, gradient search, fuzzy numbers, and analytic hierarchy process are limited in their provision of optimal solutions. This study therefore investigates a GA for weight generation and applies it to real project data. When compared to a conventional construction cost estimation model, the accuracy of the CBR- and GA-based construction cost estimation model was verified. It is expected that a more reliable construction cost estimation model could be designed in the early stages by using a weight estimation technique in the development of a construction cost estimation model.     

Time Series Models for Forecasting Construction Costs Using Time Series Indexes

Construction often involves considerable time gaps between cost estimation and on-site operations. In addition, many operations are performed over a considerable period of time. Accordingly, estimating construction costs must consider the trend of costs in the market, where construction costs normally change over time. Insight into the trend of construction costs in the market, therefore, is beneficial, even critical, to the effective cost management of construction projects. In an effort to support such insight development, two time series models were built by analyzing time series index data and comparing them with existing methods in the present study. The developed time series models accurately predict construction cost indexes. In particular, the models respond sensitively and swiftly to a quick, large change of costs, which allows for accurate forecasting over the short- and long-term periods. Overall, the models are effective for understanding the trend of construction costs.     

Statistical Analysis on the Cost and Duration of Public Building Projects

A probabilistic model is proposed to predict the risk effects on time and cost of public building projects. The research goal is to utilize a real history data in estimating project cost and duration. The model results can be used to adjust floats and budgets of the planning schedule before project commencement. Statistical regression models and sample tests are developed using real data of 113 public projects. The model outputs can be used by project managers in the planning phase to validate the schedule critical path time and project budget. The comparison of means analysis for project cost and time performance indicated that the sample projects tend to finish over budget and almost on schedule. Regression models were developed to model project cost and time. The regression analysis showed that the project budgeted cost and planned project duration provide a good basis for estimating the cost and duration. The regression model results were validated by estimating the prediction error in percent and through conducting out-of-sample tests. In conclusion, the models were validated at a probability of 95%, at which the proposed models predict the project cost and duration at an error margin of ±0.035% of the actual cost and time.     

浅议投资项目的造价控制

投资项目的造价控制贯穿于建设项目管理的全过程,有效控制建设项目的造价成本,以合理的投资建设出合格项目,对维护集团公司的经济利益和提高资金的使用效果有十分重要的实际意义。     

Ontology for Relating Risk and Vulnerability to Cost Overrun in International Projects

Risk management is about identifying risks, assessing their impacts, and developing mitigation strategies to ensure project success. The difference between the expected and actual project outcomes is usually attributed to risk events and how they are managed throughout the project. Although there are several reference frameworks that explain how risks can be managed in construction projects, a major bottleneck is the lack of a common vocabulary for risk-related concepts. Poor definition of risk and patterns of risk propagation in a project decrease the reliability of risk models that are constructed to simulate project outcomes under different risk occurrence scenarios. This study aims to extend previous studies in risk management by presenting an ontology for relating risk-related concepts to cost overrun. The major idea is that cost overrun depends on causal relations between various risk sources (namely, risk paths) and sources of vulnerability that interfere with these paths. Ontology is used to develop a database system that represents risk event histories of international construction projects and to construct a model for estimation of cost overrun. It will form the basis of a multiagent system that can be used to simulate the negotiation process among project participants about sharing of costs considering the risk allocation clauses in the contract, sources of vulnerability, and causal relations between risk events and their impacts. The ontology is constructed by interaction with Turkish contractors working in international markets and extensive literature review on risk-related concepts. The validation test results provide evidence that the ontology is fairly effective to help Turkish contractors to assess cost overrun by considering sources of vulnerability and risk in international construction projects.     

Predicting Cost Deviation in Reconstruction Projects: Artificial Neural Networks versus Regression

This paper investigates the challenging environment of reconstruction projects and describes the development of a predictive model of cost deviation in such high-risk projects. Based on a survey of construction professionals, information was obtained on the reasons behind cost overruns and poor quality from 50 reconstruction projects. For each project, the specific techniques used for project control were reported along with the actual cost deviation from planned values. Two indicators of cost deviation are used in this study: cost overrun to the owner, and the cost of rework to the contractor. Based on the information obtained, 36 factors were identified as having direct impact on the cost performance of reconstruction projects. Two techniques were then used to develop models for predicting cost deviation: statistical analysis, and artificial neural networks (ANNs). While both models had similar accuracy, the ANN model is more sensitive to a larger number of variables. Overall, this study contributes to a better understanding of the reasons for cost deviation in reconstruction projects and provides a decision support tool to quantify that deviation.     

造价监理是控制工程造价的有效途径

通过施工阶段建设监理的实践，阐明在工程建设中实行造价监理是降低工程造价的有效途径。     

论我国全过程工程造价管理的几点不足

针对我国建设项目投资决策阶段、设计阶段、施工阶段及竣工结算阶段等全过程工程造价管理过程中存在的主要问题,通过对国内建筑工程造价管理方面的经验研究,结合我国的实际情况和项目管理中成熟的造价管理经验,提出建筑工程的全过程造价管理建议,以求把工程造价控制在合理的范围和核定的限额以内。     

Application of Mathematical Matrix to Integrate Project Schedule and Cost

This research is a numerical approach to the integration of the time and cost data sets for construction projects, which addresses the conflicts between them. Mathematical matrices are introduced to show the interrelationships between the time and cost data sets and their effect on each other, and to solve the conflict due to the differences between work breakdown structure and cost breakdown structure. Several matrices of time and cost data sets of construction projects are defined. The interrelationships among the matrices are investigated and several time- and cost-related matrix equations are presented. Based on the equations, this research develops a project planning and control algorithm that estimates the expected project cost and duration and evaluates the project economy using the matrix equations derived here. The matrix application is implemented through a spreadsheet program in which the matrices are automatically connected with each other. A case study involving a housing project is performed to verify the capability and utility of this computerized matrix application in integrating project time and cost.     

Cost Optimization in Projects with Repetitive Nonserial Activities

A practical model for scheduling and cost optimization of repetitive projects is proposed in this paper. The model objective is to minimize total construction cost comprising direct cost, indirect cost, interruption cost, as well as incentives and liquidated damages. The novelty of this model stems from four main aspects: (1) it is based on full integration of the critical path and the line of balance methodologies, thus considering crew synchronization and work continuity among nonserial activities; (2) it performs time-cost trade-off analysis considering a specified deadline and alternative construction methods with associated time, cost, and crew options; (3) it is developed as a spreadsheet template that is transparent and easy to use; and (4) it utilizes a nontraditional optimization technique, genetic algorithms, to determine the optimum combination of construction methods, number of crews, and interruptions for each repetitive activity. To automate the model, macroprograms were developed to integrate it with commercial scheduling software. Details of the model are presented, and an example project is used to demonstrate its benefits.     

Data Preprocessing–Based Parametric Cost Model for Building Projects: Case Studies of Korean Construction Projects

For construction to progress smoothly, effective cost estimation is vital, particularly in the conceptual and schematic design stages. In these early phases, despite the fact that initial estimates are highly sensitive to changes in project scope, owners require accurate forecasts which reflect their supplying information. Thus, cost estimators need reliable estimation strategies. In practice, parametric cost estimation, which utilizes historical cost data, is the most commonly used method in these initial phases. Therefore, compilation of historical data pertaining to appropriate cost variance governing parameters is a prime requirement. However, data mining (data preprocessing) for denoising internal errors or abnormal values must be performed before this compilation. To address this issue, this research proposes a statistical methodology for data preprocessing. Moreover, a statistically preprocessed data–based parametric (SPBP) cost model is developed based on multiple regression equations. Case studies of Korean construction projects verify that the model enhances cost estimate accuracy and reliability than conventional cost models.     

Fuzzy Numbers in Cost Range Estimating

Range estimating is a simple form of simulating a project estimate by breaking the project into work packages and approximating the variables in each package using statistical distributions. This paper explores an alternate approach to range estimating that is grounded in fuzzy set theory. The approach addresses two shortcomings of Monte Carlo simulation. The first is related to the analytical difficulty associated with fitting statistical distributions to subjective data, and the second relates to the required number of simulation runs to establish a meaningful estimate of a given parameter at the end of the simulation. For applications in cost estimating, the paper demonstrates that comparable results to Monte Carlo simulation can be achieved using the fuzzy set theory approach. It presents a methodology for extracting fuzzy numbers from experts and processing the information in fuzzy range estimating analysis. It is of relevance to industry and practitioners as it provides an approach to range estimating that more closely resembles the way in which experts express themselves, making it practically easy to apply an approach.