Support Vector Machines Approach to HMA Stiffness Prediction

The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus (|E?|), among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive |E?| models concentrated on multivariate regression analysis of database. In this paper, SVM-based |E?| prediction models were developed using the latest comprehensive |E?| database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based |E?| model as well as the artificial neural networks (ANN) based |E?| models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization.     

Predicting Accuracy of Early Cost Estimates Using Factor Analysis and Multivariate Regression

The importance of accurate estimates during the early stages of capital projects has been widely recognized for many years. Early project estimates represent a key ingredient in business unit decisions and often become the basis for a project’s ultimate funding. However, a stark contrast arises when comparing the importance of early estimates with the amount of information typically available during the preparation of an early estimate. Such limited scope definition often leads to questionable estimate accuracy. Even so, very few quantitative methods are available that enable estimators and business managers to objectively evaluate the accuracy of early estimates. The primary objective of this study was to establish such a model. To accomplish this objective, quantitative data were collected from completed construction projects in the process industry. Each of the respondents was asked to assign a one-to-five rating for each of 45 potential drivers of estimate accuracy for a given estimate. The data were analyzed using factor analysis and multivariate regression analysis. The factor analysis was used to group the 45 elements into 11 orthogonal factors. Multivariate regression analysis was performed on the 11 factors to determine a suitable model for predicting estimate accuracy. The resulting model, known as the estimate score procedure, allows the project team to score an estimate and then predict its accuracy based on the estimate score. In addition, a computer software tool, the Estimate Score Program, was developed to automate the estimate score procedure. The multivariate regression analysis identified 5 of the 11 factors that were significant at the α = 10% level. The five factors, in order of significance, were basic process design, team experience and cost information, time allowed to prepare the estimate, site requirements, and bidding and labor climate.     

Prediction of Sewer Condition Grade Using Support Vector Machines

Assessing the condition of sewer networks is an important asset management approach. However, because of high inspection costs and limited budget, only a small proportion of sewer systems may be inspected. Tools are therefore required to help target inspection efforts and to extract maximum value from the condition data collected. Owing to the difficulty in modeling the complexities of sewer condition deterioration, there has been interest in the application of artificial intelligence-based techniques such as artificial neural networks to develop models that can infer an unknown structural condition based on data from sewers that have been inspected. To this end, this study investigates the use of support vector machine (SVM) models to predict the condition of sewers. The results of model testing showed that the SVM achieves good predictive performance. With access to a representative set of training data, the SVM modeling approach can therefore be used to allocate a condition grade to sewer assets with reasonable confidence and thus identify high risk sewer assets for subsequent inspection.     

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.     

Improving Cost Estimates of Construction Projects Using Phased Cost Factors

Central to cost-based competition is the capability to accurately predict the cost of delivering a project. Most literature on cost estimation focuses on specific estimation methods as generic techniques and little attention has been paid to the unique requirements at each project stage. This note attempts to identify the critical factors for effective estimation at various stages of typical construction projects. Drawing from organization control theory and cost estimating literature, this note develops a theoretical framework that identifies the critical factors for effective cost estimation during each project phase of a conventional construction project. The underlying logic is that as a cost estimating effort progresses, both task programmability and output measurability improve. As a result, control effort will shift from input-oriented control to a combination of output and behavior control.     

Residential Building Projects: Building Cost Indicators and Drivers

Time and costs are considered to be substantial success factors of building construction projects. In Germany, early cost estimates are provided by multiplying the cost indicator with the gross floor area. When preparing these estimates, the question arises as to which specific cost indicator has to be selected? The relevant cost drivers provide guidance for this selection. Drivers show which parameters are the determinants for the selection of the project-specific cost indicators. However, currently these drivers are not known for building construction projects in the German-speaking region. The relevant cost drivers for residential properties in Germany are identified by using regression analysis. These drivers are the median floor height, the share of the ancillary area for services, the construction duration, and the compactness of the building. Of the four cost drivers, the median floor height proved to have the greatest explanatory significance. The method proves to be suitable for answering the research question. However, some theoretically relevant drivers were not available for the properties being examined. Therefore, these drivers have to be followed up and examined during future studies. Detailed information should be included especially about materials, the planning and construction process, and specific data about various dimensions of the building.     

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.     

An Ontology for Relating Features with Activities to Calculate Costs

It is the cost estimator’s task to determine how the building design influences construction costs. Estimators must recognize the design conditions that effect construction costs and adjust the project’s activities, resources, and resource productivity rates accordingly to create a cost estimate for a particular design. Current tools and methodologies help estimators to establish relationships between product and cost information to calculate quantities automatically. However, they do not provide a common vocabulary to represent estimators’ rationale for relating product and cost information. This paper presents the ontology we formalized to represent estimators’ rationale for relating features of building product models to construction activities and associated construction resources to calculate construction costs. A software prototype that implements the ontology enables estimators to generate activities that know what feature requires their execution, what resources are being used and why, and how much the activities’ execution costs. Validation studies of use of the prototype system provide evidence that the ontology enabled estimators to generate and maintain construction cost estimates more completely, consistently, and expeditiously than traditional tools.     

Testing of Minimization of Subjectivity in Best Value Procurement by Using Artificial Intelligence Systems in State of Utah Procurement

The Performance Information Procurement System (PIPS) was tested on the procurement of the $2.96 million Bridgerland Academic Training Center (ATC) for the state of Utah Division of Facilities Construction Management. The artificial intelligence (AI) information based PIPS was run two ways—selection with biased subjectivity (similar to current best value processes) and without biased subjectivity. Unlike other best value processes, PIPS minimizes the decision-making and subjective bias of the owner’s representatives. The procurement test at Bridgerland ATC provides a comparison between the AI selection versus the user agency’s subjective prioritization. The result of the system was one of the “best” construction projects procured at the state of Utah (on time, on budget, high quality), with no contractor generated change orders for additional cost, minimized construction management requirements, and high customer satisfaction.     

International Comparison of Cost for the Construction Sector: Purchasing Power Parity

The importance of the construction sector in national economies around the globe and the global nature of the industry require a prudent international comparison of construction costs. From the view of international construction ventures, cost comparisons have generally been accomplished using published currency exchange rates. Global organizations dealing with development aid and the comparison of the gross domestic product (GDP) of nations have used an approach that has its roots in established econometric theories. This approach is based on the Casselian purchasing power parity (PPP) doctrine that essentially conducts the comparison based on the local purchasing power of currencies, as opposed to exchange rates. The World Bank, which conducts the GDP comparison, uses the PPP-based approach to compare construction sector output. This paper provides an overview of the background and application of PPP and its use for international cost comparisons conducted for various nations. Methods currently used for construction cost comparisons are reviewed. A critical review of domestic construction cost comparison approaches is provided with the intent to identify the key differences between temporal and spatial comparisons. Case studies of construction cost factors are used to demonstrate the importance of PPP-based cost comparisons for construction economics.     

Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems

This paper compares the performance of three optimization techniques, namely feature counting, gradient descent, and genetic algorithms (GA) in generating attribute weights that were used in a spreadsheet-based case based reasoning (CBR) prediction model. The generation of the attribute weights by using the three optimization techniques and the development of the procedure used in the CBR model are described in this paper in detail. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of 29 residential building projects. The results indicated that GA-augmented CBR performed better than CBR used in association with the other two optimization techniques. The study is of benefit primarily to researchers as it compares the impact attribute weights generated by three different optimization techniques on the performance of a CBR prediction tool.     

Risk-Based Safety Impact Assessment Methodology for Underground Construction Projects in Korea

Safety of construction projects may be affected by various factors such as types and scale of projects, construction methods, safety management procedure, climate, site conditions, etc. Among them is the quality of design in relation to safety. Presently, however, designers typically are not involved in construction safety. They are often uncertain of their responsibilities in relation to construction safety and fail to be responsible for avoiding or reducing safety-related risks. In this study, the concept of safety impact assessment to achieve “design-for-safety” in the design phase is introduced. For this purpose, a safety impact assessment model was devised, and a methodology using the risk-based safety impact assessment approach for open-cut type underground construction projects in Korea is suggested. The suggested methodology includes a safety information survey, classification of safety impact factors caused by design and construction, and quantitative estimation of magnitude and frequency of safety impact factors. A checklist which can be easily used for assessing the safety performance of design products is also proposed. A real-world case study on the safety impact assessment of a subway construction project in Korea is also provided in the paper.     

柳钢工程管理中的造价控制措施

从工程招标、施工、结算等阶段总结控制工程造价的措施。     

Entropic Risk Analysis by a High Level Decision Support System for Construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.     

Developing Cost Response Models for Company-Level Cost Flow Forecasting of Project-Based Corporations

The difficulty in applying the standard curve (S-curve) and cost-schedule integration (CSI) techniques for company-level cost flow forecasting in a project-based industry is the prerequisite of forecasting future unknown individual projects and contract classifications. By analyzing cost flows at the company level through a pool of macroeconomic and internal financial data, this paper proposes an innovative approach to firm-specific model estimation. First, a series of data transformations introduce linear relationships between cost, macroeconomic, and internal financial variables. Second, multivariate regression analysis is employed for initial model building. Third, for the purposes of model restructuring, a subsequent application of Yule–Walker estimates and incomplete principal component analysis is used. This paper uses a sample of four project-based construction firms to demonstrate model performance. Using this methodology, mean absolute percentage error (MAPE) values of the forecasting models range from 0.27 to 0.60%. As such, the transformed cost, macroeconomic, internal financial data could strongly predict company-level cost flow forecasting. While converting the predicted cumulative cost data to periodic cost flows, the MAPE values were augmented, ranging from 7.04 to 17.55%, thus, requiring future research.     

多支持向量机在高炉喷煤规则挖掘中的应用

高炉冶炼受生产过程运行规律和生产技术人员操作方式支配,由于对高炉的认识还不充分,在高炉冶炼操作过程中有时忽略了工艺指标与参数之间的相互关系,造成能源的消耗。寻求高炉冶炼喷煤过程的操作规则对生产节能降耗就显得尤为重要。选取蕴含有客观规律和生产技术人员操作决策模式等重要信息的生产历史数据,选出满足优化标准的数据,运用模糊C均值聚类将数据分成子集,分别建立相应的支持向量机子模型,将输入对应每一类的隶属度值再作为权值进行加权求和得到模型最终的输出值,从而建立起高炉冶炼喷煤的操作规则模型。通过规则挖掘实现对高炉现场冶炼过程的指导,达到优化高炉生产操作模式的目的。     

Application of a Sustainability Model for Assessing Water Main Replacement Options

Sustainable development, conceived as a new and multidisciplinary paradigm, is receiving much attention throughout the global community. The purpose of this paper is to apply the sustainability assessment model (SAM), an assessment and decision making methodology, to a water main replacement project in an urban environment to determine the most sustainable project alternative among three possible options. This case study presents the use of SAM in considering various multicriteria sustainability indicators while working towards achieving sustainability enhancement. Objectives of sustainability enhancement include: (1) minimizing environmental impact; (2) maximizing economical benefit and output; (3) social and cultural conservation and promotion; and (4) satisfying basic requirements such as structural soundness and capacity. Six assessment methods including the analytic hierarchy process, cost, pollution, energy, time estimation, and natural resource depletion analysis are used for both qualitative and quantitative sustainability indicators. The weighted sum model is then utilized to integrate the six independent assessment results to elicit the final decision.     

Using Weibull Analysis for Evaluation of Cost and Schedule Performance

Large amounts of money are lost each year in the construction industry because of poor schedule and cost control. Few contractors specify and follow systematic schedule monitoring practices. Traditionally, the earned value method (EVM) is used to control and monitor schedule performance using the schedule and cost performance indices which compare the budgeted cost of work performed to what was originally scheduled or what is actually expended. This paper presents a statistical approach, namely Weibull analysis, to evaluate stochastically the schedule performance of construction or design projects. The approach can be used in conjunction with the EVM to enhance the evaluation and control of schedule performance. Weibull analysis is a common method for failure analysis and reliability engineering used in a wide range of applications. In this paper, the applicability of Weibull analysis for evaluating and comparing the reliability of the schedule performance of multiple projects is presented. The various steps in the analysis are discussed along with an example in which two projects are analyzed and compared. The authors conclude that Weibull analysis has several advantages and provides a relatively robust and effective method for construction managers to better control and monitor their projects.     

Using Decision Trees for Determining Attribute Weights in a Case-Based Model of Early Cost Prediction

This paper compares the performance of three different decision-tree-based methods of assigning attribute weights to be used in a case-based reasoning (CBR) prediction model. The generation of the attribute weights is performed by considering the presence, absence, and the positions of the attributes in the decision tree. This process and the development of the CBR simulation model are described in the paper. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of residential building projects. The CBR results indicate that the attribute weights generated by taking into account the information gain of all the attributes performed better than the attribute weights generated by considering only the appearance of attributes in the tree. The study is of benefit primarily to researchers, as it compares the impact of attribute weights generated by three different methods and, hence, highlights the fact that the prediction rate of models such as CBR largely depends on the data associated with the parameters used in the model.