Jae-Ho Pyeon

Schedule Effectiveness of Alternative Contracting Strategies for Transportation Infrastructure Improvement Projects

Incentive/Disincentive (I/D) and Cost-plus-Time (A+B) are the two most widely used alternative contracting strategies to implement public transportation infrastructure projects in the United States. However, little is known about their impact on project schedule performance. As a result, state highway agencies often struggle to select effective contracting strategies and estimate the budgets accurately for transportation projects. A quantitative analysis drawing on 1,372 public transportation projects in California was conducted to address this shortcoming by determining the schedule effectiveness of alternative contracting strategies. The analysis showed that I/D contracting strategy was effective in shortening overall construction duration, however, A+B contracting strategy led to severe schedule delays, worse than conventionally contracted projects. The findings of this study will assist decision-makers, particularly state highway agencies, make better-informed decisions when they are planning and sele...

Effectiveness Study of Methods for Removing Temporary Pavement Markings in Roadway Construction Zones

AbstractPavement markings provide guidance to road travelers and, when not properly removed during construction projects, can lead to fatal accidents. Current state guidelines, however, do not specify a level of acceptable damage and there is no reliable inspection tool available other than on-site individual visual inspections. To address these shortcomings, the main objective of this study was to identify effective temporary marking removal methods on pavements in roadway construction zones. Unlike previous studies, this research tested an extensive number of removal methods on both concrete and asphalt pavements, especially including dry-ice blasting and environmentally friendly non-MeCl chemical stripping. The methods were selected based on a nationwide survey and literature review. In addition, an innovative removal inspection method was developed using image-processing technology to accurately measure the visual damage to the pavement. Among the tested removal methods, the new non-MeCl chemical stri...

A Method of Assigning Weight Values for Qualitative Attributes in CBR Cost Model

For construction projects, the importance of early cost estimates is highly recognized by the project team and sponsoring organization because early cost estimates are frequently a foundation of business decisions as well as a basis for identifying any changes as the project progresses from design to construction. However, it is difficult to accurately estimate construction cost in the early stage of a project due to various uncertainties in construction. To deal with these uncertainties, cost estimates should be made several times over the course of the project. In particular, early cost estimates are essential process for successful project management. For accurate construction cost estimates, it is necessary to compare cost estimates with actual costs based on historical project data. In this context, case-based reasoning (CBR), which is the process of solving new problems based on the solutions of similar past problems, can be considered as an effective method for cost estimating. To obtain this, it is also required to define the attribute similarities and the attribute weights. However, no existing method is capable of determining attribute weights of qualitative variables. Consequently, it has been a well-known barrier of accurate early cost estimates. Using Genetic Algorithms (GA), this research suggests the method of determining the attribute weight of qualitative variables. Based on building project case studies, the proposed methodology was validated.

Hazardous Area Identification Model using Automated Data Collection(ADC) based on BIM

A considerable number of construction disasters occurs on pathway. A safety management in construction sites is usually performed to prevent accidents in activity areas. This means that safety management level of hazards on pathway is relatively minified. Many researchers have introduced that a hazard identification is fundamental of safety management. Thus, algorithms for helping safety managers` hazardous area identification is developed using automated data collection technology. These algorithms primarily search potential hazardous area by comparing workers` location logs based on real-time locating system and optimal routes based on BIM. And potential hazardous areas is filtered by identified hazardous areas and activity areas. After that, safety managers are provided with information about potential hazardous areas and can establish proper safety countermeasures. This can help improving safety in construction sites.

Proposed Methodology for Dynamic Schedule Compression

Despite aggressive efforts of project managers to maintain a project schedule or to recover from a lapsed schedule, delays and cost overruns have become routine phenomenon at many construction projects. This research proposes a proactive schedule compression method to reduce the expected project completion time by removing latent lazy time caused by constraints that impose non-value-added effects on project. An additional objective of this research is to develop a systematic environmental model, the dynamic schedule compression model (DSCM), to improve performance against latency and complexities of design and construction projects in schedule compression. Developing and implementing the DSCM model will result in the following research outcomes: optimizing schedule compression concepts and methods to minimize waste and maximize project performance; detecting and eliminating latent lazy time that project has potentially; managing latency caused by schedule compression; understanding of schedule compression frameworks; and developing system dynamics-based schedule compression model.

Developing an Optimization Model and Program for Planning the Earthwork Based Upon Transportation Theory

During road construction, minimizing haul and return distances as well as keeping a balance between cut and fill quantities are two of the key tasks for earthmoving operation planning. The result of the earthwork planning has a significant impact on the construction cost and duration. Although there have been research efforts regarding optimized earthwork planning using linear programming, the current practice of selecting earthwork planning methods typically depends on a field manager`s intuitive and/or experimental knowledge. Furthermore, there is no system considering earthwork influential field factors including the transportation distance, the earthwork quantity, and the recycling ratio of earth volume. Therefore, this research focuses on the development of such a model for planning the optimized earthwork to increase the efficiency of a road construction. The proposed model is developed based upon the transportation problem method which is a part of Linear Programming. The application result of optimization model on a case study shows that the duration and cost for earthwork ha sbeen reduced approximately 19% and 11% respectively

Clarifying Big Data: The Concept and Its Applications

This paper clarifies the concept of Big Data with a discussion of its managerial implications and presents its defining characteristics differentiating Big Data with traditional analytics. This paper also introduces the concept of Big Data in the context of three industries, namely, finance, supply chain and marketing and discusses how this concept can be applied in the business world. With regard to this concept, fundamental yet critical discussions were made for any further understanding of Big Data. Finally, this paper contributes to our current knowledge of Big Data by relating and contrasting Big Data to traditional analysis while presenting context specific discussions for its applications. Although technical aspects of Big Data were not covered in this paper, this paper focused on serving as a business discussion for the concept of Big Data. For future work, business contents must be related to technical capabilities and solutions in order to provide a better understanding of Big Data.

Simulation Model to Predict Project Time and Cost Performance of Incentive/Disincentive Construction Projects

In highway construction, Incentive/Disincentive (I/D) contracting provisions have been widely used to motivate the contractor for early completion of roadway construction in the United States. Although I/D provisions are one of the owner’s useful management tools, there is no systematic decision support system for the selection of I/D projects. This paper presents a project time and cost performance simulation model to assist project planners and managers by providing a complete picture during the I/D contracting decision-making process of possible performance outcomes with probabilities based on historical data. This study was performed by collecting transportation construction project data. The collected project data from the Florida Department of Transportation were evaluated using time and cost performance indices and then statistical data analysis was performed to identify important factors that influence construction project time performance. Using Monte Carlo simulation procedures, this study demonstrated a methodology for developing an I/D project time and cost performance prediction model. User-friendly visual interfaces were developed to perform the simulation and report results using Visual Basic Application programming. The developed model was validated using additional cases of transportation construction projects.