Journal of Construction Engineering and Management

Rapid growth of residential construction in the city of Edmonton, Alberta, Canada has led to an increased demand for lot grading (LG) inspectors. The traditional inspection process has contributed to inspection delays, backlog, and decreased customer satisfaction. The aim of the research presented in this paper is to study, evaluate, and optimize the existing LG approval process. Data from past years were collected and analyzed to establish a baseline performance level. Flowcharts were developed to visualize the process and a survey was conducted to determine potential areas of improvement from the perspectives of different shareholders. Lean thinking principles were applied to identify and reduce the nonvalue added activities and improve performance. The final phase of the research involved the simulation of the process to evaluate the impact of the implementation of various recommendations. Based on the simulation results, final recommendations were made and later implemented with the help from the city’s Information technology department. These modifications to the existing process produced significant gains in quality and productivity, as predicted by the simulation model.

The demand for urban underground space has been increasing in the past decades to create living space and to avoid traffic congestion. A critical concern during the design and development of the underground space is the influence of construction-related ground movements on neighboring facilities and utilities. Currently, engineers can estimate ground movements using a combination of semiempirical methods and numerical model simulation. However, these advanced analyses require accurate as-built construction staging data, which most projects lack. The traditional approach of collecting construction-staging data is both labor intensive and time consuming. This paper explores the use of three-dimensional laser scanning technology to accurately capture construction activities during development of an urban excavation. The paper describes the planning, execution, and data processing phases of collecting accurate construction as-built staging information over a period of $4\text{months}$ at an urban excavation site in Evanston, Ill. The resulting data provide an unprecedented level of detail on the as-built site conditions and provide much needed information to civil engineering disciplines involved in an urban excavation including construction management and structural and geotechnical engineering.

Contractor prequalification involves the screening of contractors by a project owner to determine their competence to complete the project on time, within budget, and to expected quality standards. The process of prequalification involves a large number of contractors, each being represented by many attributes. A neural network model was applied to aid in the prequalification process by classifying contractors into groups based on similarity in performance using the financial ratios of liquidity, activity, profitability, and leverage. Contractors are represented in this model by patterns in four-dimensional space. Patterns of similar performance tend to form clusters intercepting regions of low pattern density in between. A neuron with weights is used as a classifier to set a decision boundary between clusters. The method basically iterates the neuron weights to move the decision boundary to a place of low pattern density. Then, the statistical hypothesis testing of the mean difference of two independent samples was used to validate the classification of the parent class to the two child classes considering the four ratios separately. The method was used hierarchically to classify a group of 245 contractors into classes of small numbers. Finally, the inferred procedure of classification proves that the neural network model classified the four-dimension pattern representing contractors efficiently.

Decision trees (DTs) have proven to be valuable tools for decision making. The common approach for using DTs is calculating the expected value (EV) based on single-number estimates, but the single-number EV method has limited the DTs’ real-life applications to a narrow scope of decision problems. This paper introduces the stochastic multilevel decision tree (MLDT) modeling approach, which is useful for analyzing decision problems characterized by uncertainty and complexity. The MLDT’s advantages are shown through a computer simulation program: the Decision Support Simulation System (DSSS). The DSSS allows users to model probabilistic linear graph networks and provides a hierarchical modeling method for modeling decision trees to present uncertainties more accurately. It consists of three modules: tree analysis networks (TANs), the shortest and longest path dynamic programming analysis network, and cost time analysis networks. The paper only discusses the TAN module by presenting the MLDT concept under the TAN of the DSSS computer application. The content of the paper includes the modeling approach, its advantages, and examples that can be used in modeling stochastic trees. The DT-DSSS was verified by conducting several tests and validated by using it extensively for undergraduate courses in civil engineering at the University of Calgary for the last two academic years.

This study proposes an integration of particle swarm optimization (PSO) and a construction simulation so as to determine efficiently the optimal resource combination for a construction operation. The particle-flying mechanism is utilized to guide the search process for the PSO-supported simulation optimization. A statistics method, i.e., multiple-comparison procedure, is adopted to compare the random output performances resulting from the stochastic simulation model so as to rank the alternatives (i.e., particle-represented resource combinations) during the search process. The indifference zone and confidence interval facilitate consideration of the secondary performance measure (e.g., productivity) when the main performance measures (e.g., cost) of the competing alternatives are close. The experimental analyses demonstrate the effectiveness and efficiency of the proposed simulation optimization. The study aims to providing an alternative combination of optimization methodology and general construction simulation by utilizing PSO and a statistics method so as to improve the efficiency of simulation in planning construction operations.

This paper describes an objective process to select appropriate information technologies (ITs) for construction applications using the multiatttribute utility theory. Construction industry IT managers often find it difficult to objectively evaluate IT systems because each system has its own technical, economic, and risk considerations. This paper uses a multi-attribute utility theory to facilitate the decision-making process in such complex decisions. The theory’s basic premise is that the selection issue can be broken down into alternative attributes. Based upon the user’s tradeoffs among attributes, important weights are quantified and single-attribute utilities are measured. Finally, single-attribute utilities are combined to develop one single aggregate utility index for each alternative. A test is performed to use the utility theory to evaluate two different labeling approaches (radio-frequency identification and bar codes) for a construction material testing laboratory. Results showed that this technique has the ability to make decisions more objectively. Overall, the suggested model can also be adapted to evaluate other construction-related decisions, pertaining to other IT applications, equipment selection, and construction methods and techniques.

This paper describes a process for converting a project manager’s qualitative evaluation of “successful performance” to a quantitative measurement. In a recent study, randomly selected electrical companies throughout the United States submitted two projects for analysis: (1) one project that was well planned and performed successfully; and (2) one project that was poorly planned and performed less-than-successfully. Overall, 55 projects were evaluated. Success, however, is a subjective concept that often depends on the team member providing the definition. Consequently, during the interview process, project managers were asked to provide their own definition of successful performance, and this definition was matched to the collected data to identify appropriate variables for inclusion in a performance measurement index. A reliability analysis was performed, and six variables were ultimately used to construct the index, including actual percent profit, percent schedule overrun, amount of time given, communication between team members, budget achievement, and change in work hours. A cross-validation technique was used to evaluate the performance measurement index, and a misclassification rate of 2% was observed, which indicated that the model was useful for quantitatively measuring successful performance based on the project managers’ definitions of success.

Facility managers face the challenges of managing many different types of small, geographically dispersed construction projects. Depending on the complexity and distribution of projects, the time required to prepare for production consumes a large percentage of the total time required to complete the job. Increasing crews’ productive hours is a key objective when planning multiproject schedules. Existing methods, however, lack the effective means to explicitly model, analyze, and optimize resource utilization for these multiple concurrent projects. As a result, few facility managers fully exploit the potential to better manage their often limited budget and resources. This paper presents an explicit model of the mobilization requirements of multiple crews performing a variety of different activities over a geographic space. The model allows the facility manager to explicitly investigate the impact of crew composition, crew specialization, and depot locations. Using work rule decisions regarding alternative crew allocations, facility managers may dynamically allocate resources to optimize resources and to complete projects in a minimum amount of time. To verify and validate this new model, a computerized system, called FIRS (Facility/Infrastructure Resource Scheduler), was created to analyze the multiproject resource plans with data from two military organizations and a university campus. FIRS utilizes a new genetic algorithm that was developed specifically to work with multiproject scheduling. Using FIRS, facility managers can develop and test alternative crew allocations based on the qualifications of the crews available and the type of operation being performed.

The competitive nature of the construction industry has motivated many specialty contractors to search for ways to improve efficiency by increasing their quality and decreasing their costs in order to strengthen their market share. As a result, contractors are turning to “better planning” as a method for improving their efficiency and, consequently, increasing their profitability. In fact, a consensus exists in the construction industry that more formalized preconstruction planning is necessary to remain successful in an increasingly competitive industry. This paper presents a model electrical preconstruction planning process that was crafted from outstanding processes used on several successful electrical projects. Furthermore, a method to evaluate the effectiveness of planning, by comparing actual planning to the model process, is briefly introduced. From this assessment, “effective planning” was correlated to project outcome, and evidence is provided that better planning is, indeed, related to successful performance.

Evaluating construction requirements should be an indispensable part of improving the constructability of a facility project. However, a number of existing function analysis approaches focus mainly on the final functions with respect to the finished facility. The construction industry still lacks methodologies to represent and evaluate the functional construction requirements for constructability analysis. This paper develops the concept of intermediate functions for representing the construction requirements from three perspectives, namely, function user, function provider, and the temporal and spatial relationships between the user and the provider. This is followed by the development of an in-progress product model to abstract the construction lifecycle of a facility product utilizing the concept of component state. Furthermore, the reference relationships among such perspectives as in-progress product, intermediate function, resource, and process are addressed. Based on these reference relationships, the bottleneck states for providing the intermediate functions can be detected. A case study of constructing the deck of a bridge using the balanced cantilever approach is presented to illustrate the utilization of this concept and representation.