Cost Information Model for Managing Multiple Projects

Construction companies must deal with several projects at once, but a system to manage multiple projects is not fully developed yet. The first step towards developing such system is to design an information model that is suitable for managing multiple projects. This paper presents the cost-based project modeling (CBPM) method in contrast to the traditional activity-based project modeling methods. The CBPM uses cost as a core of the model along with other project information organized around it. The CBPM serves as a platform for integrating project information from multiple projects. Various types of construction costs are hierarchically modeled to generate corporate-wide information such as project performances, cash flows, and other predictive indicators. Based on the information model, an object-oriented database was developed to contain cost data across several projects. In the model, a module that connects to external systems is built into the model to enhance interactivity with the legacy systems and the industry standards. A prototype system was developed and tested with actual project data to validate the information processing capabilities of the model. The findings from the test indicate construction cost can be an excellent medium that can organize various types of information of multiple projects.     

Automated Generation of Customized Field Data Collection Templates to Support Information Needs of Cost Estimators

While estimating activity production rates, cost estimators rely on historical production rates. To have realistic and useful cost estimates based on historical production rates, such production rate data should be augmented with historical contextual information that depict conditions under which activity production rates were achieved in past projects. This information is needed in determining which production rate to use among alternates for a similar activity existing in a new bid. Estimators need contextual information especially when they are unfamiliar with the work being estimated. Hence, such information items need to be identified, collected, and stored for estimators’ use in new projects. This paper details a construction-method specific and an extensible approach that is developed for enabling cost estimators to define contextual information items that need to be collected on job sites and stored as part of project histories. Based on this approach, the writers implemented a prototype system, called as ContextGen, and performed user-tests with estimators with different experience levels. Results showed that the developed approach captures method-specific information needs of estimators and is extensible to incorporate new contextual information items that can have different data representations. The developed approach is also precise in retrieving contextual information items specific to a construction method from a set of predefined contextual information items available in a library.     

Action Research as a Proactive Research Method for Construction Engineering and Management

Academic research in applied disciplines such as construction engineering and management (CEM) has the dual mission of simultaneously contributing to the solution of practical problems and creating theoretical and conceptual knowledge. To do so, appropriate research approaches are needed. However, extant literature in the field has paid little attention to this issue and research methods used have been almost entirely either quantitative surveys or case studies. In this paper, action research (AR) is proposed as an answer to this knowledge gap. AR aims at building and testing theory within the context of solving an immediate practical problem in a real setting. The paper describes the underlying philosophy and application procedure of AR and highlights its strengths and weaknesses. Then, the applicability of the method to CEM is illustrated through a case study of improving access to information to support planning and decision making in a construction owner organization through designing and implementing a data warehouse. The findings indicate that AR is a reliable, structured, and rigorous research approach that is very useful for conducting applied research in construction and enabling academia to influence and improve construction industry practices. It can also effectively help to improve collaboration between academic researchers and industry practitioners in research and development projects.     

Decision Tool for Selecting the Optimal Techniques for Cost and Schedule Reduction in Capital Projects

Projects must meet budget, schedule, safety, and quality goals to be regarded as a success. Many factors come into play, and many decisions are made that influence a project’s outcome. Today, owners are often faced with deciding between an execution strategy that emphasizes either project cost or project schedule. Such a decision may be made not once, but throughout the life of the project. Project teams, when required by the owner to make a cost-schedule trade-off, generate common sense ideas, best practices, and other means of achieving the desired trade-off through brainstorming sessions. This paper offers a tool for systematically identifying the techniques which are most effective in achieving the trade-off goal. Project teams can then use these techniques in a timely fashion to increase the likelihood of project success.     

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.     

Integrating Neurofuzzy System with Conceptual Cost Estimation to Discover Cost-Related Knowledge from Residential Construction Projects

Cost estimation during early stage of a building construction project plays an important role for feasibility analysis in the planning and design phase. Traditional knowledge-based approaches suffer an essential difficulty due to resource price fluctuation in the market. This paper presents a hybrid method that integrates the principal items ratio estimation method with the adaptive neurofuzzy inference system for mining of cost estimation data. The proposed method provides exceptional capability for mining estimation knowledge that is difficult to be discovered by traditional knowledge-based approaches. A case study of residential building projects in China is conducted to demonstrate the proposed method. The testing results show that the proposed method does not only achieve high estimation accuracy, but also provide desirable features for estimators, such as explicit fuzzy decision rules and graphical presentations.     

Construction Engineering Requirements for Integrating Laser Scanning Technology and Building Information Modeling

Laser scanning for rapid spatial data acquisition is an established technology in the architecture, engineering, and construction (AEC) sector with a wide range of applications. An understanding of the wide variation of technical requirements and considerations associated with these applications is critical to decision making about laser-scanning implementation on projects. Furthermore, significant industry transformations in the use of building information modeling present extraordinary opportunities for AEC professionals to employ the use of laser scanning in the context of holistic, collaborative workflows grounded in three-dimensional model-based design. This report analyzes the construction engineering requirements of laser scanning technology for applications across all phases of the project life cycle and proposes a multidisciplinary framework to integrate applications of laser scanning technology with the fundamentals of three-dimensional model-based design.     

Construction Engineering Process and Knowledge Requirements for Fostering Creative Design Solutions on Infrastructure Projects

Construction engineering for major infrastructure projects covers a wide range of activities to evaluate and select the techniques for assembling materials and components. Construction engineering inherently presents a very challenging opportunity for creative design, particularly on infrastructure projects. This construction engineering activity can be described as one of creating and developing workable, cost-effective, low-risk technical solutions for an array of infrastructure construction problems that must be solved from the plans and specifications stage through facility completion. The purpose of this paper is to illustrate a 10-step construction engineering process and define important knowledge requirements to foster creative design solutions using four case studies, including (1) positioning and holding a concrete bridge caisson in a 7-knot tidal current for a 4-month period; (2)?skidding a 55,000-t immersed tube tunnel element 200?m on dry land from casting site to launch site; (3)?building a major dam without the use of river diversion or on-site dewatering systems; and (4)?building underwater bridge piers without the use of conventional bottom-founded cofferdams. The creative design process was able to successfully devise a plan for solving highly technical construction challenges using a process-based approach. The key requirements of knowledge, skill, and experience necessary to perform these activities are presented to assist construction engineers in preparing for these creative opportunities.     

Using Group Decision Support System to Support Value Management Workshops

A group decision support system (GDSS) is an interactive computer-based information system that combines the capabilities of communication technologies, database technologies, computer technologies, and decision technologies to support the identification, analysis, formulation, evaluation, and solution of semistructured or unstructured problems by a group in a user-friendly computing environment. As there is a strong demand for improvements to the practice of value management (VM), research has been conducted to design a GDSS prototype system, named the interactive value management system (IVMS), to explore its potential application in VM workshops and to investigate the effect of the application. The paper begins with an introduction to the problems of implementing VM in the Hong Kong construction industry and then proceeds to an illustration of the features of the proposed system, which has been developed in the research. Two validation studies designed to test the support of the proposed system are described and the results discussed. Findings from this research indicate that IVMS is supportive in overcoming the problems and difficulties in VM workshops.     

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.     

Dynamic Knowledge-Based Process Integration Portal for Collaborative Construction

There is a need to support collaborative coordinated construction through the integration of work processes across various stakeholders, disciplines, and projects. Due to the fast-growing complexities of domain-wide coordination and collaboration, coupled with the emerging opportunities of knowledge sharing, a knowledge-based approach is believed to be the way to go. Knowledge-based process integration takes into account explicit and formal semantics (meaning) of processes. It aims at supporting seamless integration of knowledge-carrying processes. This paper presents a prototype collaborative portal for domain-wide process integration. The portal is ontology-based: semantic process representation is enabled through ontologies, and knowledge-based process integration is facilitated through an ontology merger. The portal aims at offering an efficient value-adding collaboration platform through multidimensional process integration for producing a common workflow that goes beyond listing of processes to a knowledge-enabled view. The prototype was evaluated through a focus group.     

Estimating Performance Time for Construction Projects

The prediction of performance time for construction projects is a problem of interest to both researchers and practitioners. This research seeks to gain insight into the significant factors impacting construction duration by developing a regression model. Data were collected for 856 facility projects completed between 1988 and 2004. These data were analyzed using Bromilow’s time-cost (BTC) model (1969) as well as multiple linear regression. The multiple linear regression model was found to provide the most acceptable prediction. As in the BTC model and previous research reported in the literature, a significant correlation was found to exist between cost and duration. However, several other factors were also identified that resulted in significantly lower than average construction durations. These include projects completed within certain management groupings, managed by a certain construction agent, and designed by in-house personnel.     

Considering Prework on Industrial Projects

Prefabrication, preassembly, modularization, and off-site fabrication, collectively termed as prework have become more viable with recent advances in design and information technologies. These construction methods offer a substantial opportunity to improve project performance when circumstances merit. Successful implementation of these methods on a particular project requires systematic analysis and early decision making based on specific factors of the project. This paper identifies those factors influencing decisions on the use of prework, and current industry practices for evaluating the applicability of prework on industrial projects. It then presents a decision framework to assist industry practitioners with evaluating the applicability of prework on their project, and describes a computerized tool to aid project teams in the decision-making process. The developed framework and tool are useful and effective in the decision-making process, and easy to use, as validated by practitioners in the industry.     

Multimodal Image Retrieval from Construction Databases and Model-Based Systems

In the modern and dynamic construction environment it is important to access information in a fast and efficient manner in order to improve the decision making processes for construction managers. This capability is, in most cases, straightforward with today’s technologies for data types with an inherent structure that resides primarily on established database structures like estimating and scheduling software. However, previous research has demonstrated that a significant percentage of construction data is stored in semi-structured or unstructured data formats (text, images, etc.) and that manually locating and identifying such data is a very hard and time-consuming task. This paper focuses on construction site image data and presents a novel image retrieval model that interfaces with established construction data management structures. This model is designed to retrieve images from related objects in project models or construction databases using location, date, and material information (extracted from the image content with pattern recognition techniques).     

Construction Project Risk Assessment Using Existing Database and Project-Specific Information

This paper develops a risk assessment methodology for construction projects by combining existing large quantities of data and project-specific information through updating approaches. Earlier studies have indicated that risk assessment is still difficult for practicing engineers to use due to the requirement of data on too many input variables. However, the availability of existing large quantities of data and project-specific information makes it possible to simplify the risk assessment procedure. Two main ideas are pursued in this paper to facilitate practical implementation: identify and evaluate the critical risk events, and develop a systematic updating methodology. Both epistemic and aleatory types of uncertainties in the data are considered, and corresponding updating procedures are developed. The proposed methodology is illustrated for the construction risk assessment of a cable-stayed bridge.     

Evaluation of Risk Factors Leading to Cost Overrun in Delivery of Highway Construction Projects

Accurate owner budget estimates are critical to the initial decision-to-build process for highway construction projects. However, transportation projects have historically experienced significant construction cost overruns from the time the decision to build has been taken by the owner. This paper addresses the problem of why highway projects overrun their predicted costs. It identifies the owner risk variables that contribute to significant cost overrun and then uses factor analysis, expert elicitation, and the nominal group technique to establish groups of importance ranked owner risks. Stepwise multivariate regression analysis is also used to investigate any correlation of the percentage of cost overrun with risks, together with attributes such as highway project type, indexed cost, geographic location, and project delivery method. The research results indicate a correlation between the reciprocal of project budget size and percentage cost overrun. This can be useful for owners in determining more realistic decision-to-build highway budget estimates by taking into account the economies of scale associated with larger projects.     

Assessing Residual Value of Heavy Construction Equipment Using Predictive Data Mining Model

Construction equipment constitutes a significant portion of investment in fixed assets by large contractors. To make the right decisions on equipment repair, rebuilding, disposal, or equipment fleet optimization to maximize the return of investment, the contractors need to predict the residual value of heavy construction equipment to an acceptable level of accuracy. Current practice of using rule-of-thumb or statistical regression methods cannot satisfactorily capture the dynamic relationship between the residual value of a piece of heavy equipment and its influencing factors, and such rules or models are difficult to integrate into a decision support system. This paper introduces a data mining based approach for estimating the residual value of heavy construction equipment using a predictive data mining model, and its potential benefits on the decision making of construction equipment management. Compared to the current practice of assessing equipment residual values, the proposed approach demonstrates advantages of ease of use, better interpretability, and adequate accuracy.     

CB-Contract: Case-Based Reasoning Approach to Construction Contract Strategy Formulation

The formulation of an appropriate contract strategy is a fundamental yet critical aspect of contractual arrangement for a project. This paper presents a decision support system (DSS) prototype for contract strategy formulation using the case-based reasoning (CBR) approach. The prototype is called CB-Contract. An overview on the scope and formulation process of contract strategy is provided. Contract strategy is regarded as comprising four main substrategies: (1) work packaging; (2) functional grouping; (3) contract type; and (4) award method. The significance of CBR in contract strategy formulation is examined. The overall architecture of CB-Contract is discussed, with particular emphasis on its knowledge component. A case structure for case representation has been suggested taking into account the important factors that need to be considered while formulating a contract strategy, in particular project characteristics, client’s objectives, and client’s comparative advantages. Issues pertaining to case indexing and similarity calculation for efficient retrieval of similar cases are explored. An illustrative example to obtain similar cases for a given set of project information is presented. Case adaptation that considers the robustness of each substrategy, the compatibility of the substrategies, and the expected effectiveness of the contract strategy is also included in the illustrative example.     

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.     

Material-Based Construction Site Image Retrieval

The technological advancements in digital imaging, the widespread popularity of digital cameras, and the increasing demand by owners and contractors for detailed and complete site photograph logs have triggered an ever-increasing growth in the rate of construction image data collection, with thousands of images being stored for each project. However, the sheer volume of images and the difficulties in accurately and manually indexing them have generated a pressing need for methods that can index and retrieve images with minimal or no user intervention. This paper reports recent developments from research efforts in the indexing and retrieval of construction site images in architecture, engineering, construction, and facilities management image database systems. The limitations and benefits of the existing methodologies will be presented, as well as an explanation of the reasons for the development of a novel image retrieval approach that not only can recognize construction materials within the image content in order to index images, but also can be compatible with existing retrieval methods, enabling enhanced results.