Technical Memorandum on Innovative Contracting Methods Implementation Studies

The main objective of this study is to determine the effectiveness of alternative contracting strategies on aspects of project performance such as schedule and cost, in order to promote ways to apply the strategies effectively. A quantitative analysis that draws on 1,372 infrastructure improvement projects recently completed in California from 2000 to 2008 was conducted to achieve the objective. According to the analysis, alternative projects contracted with I/D and A+B represented 7% of all project establishments and 23% of all project allotment costs. The results of one way ANOVA analyses show that I/D projects held a decisive schedule-saving advantage over A+B and conventional projects, but that I/D also increased project costs significantly more than the others because of a higher frequency of contract change orders. The results of statistical analyses reveal a severe effectiveness problem with use of the A+B contracting strategy. When compared with conventional projects, A+B not only included extreme severe schedule overruns, but it also increased project costs far above the levels seen in conventional projects; both of these resulted from inaccuracies created by allowing contractors to bid on contract time. According to the analysis, the additional cost growth for utilizing I/D was recouped by reduced construction time, but this tradeoff was not seen in A+B projects

Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6\u27s states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6\u27s State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters

Selecting the Most Feasible Construction Phasing Plans for Urban Highway Rehabilitation Projects

Despite the abundance of research that has aimed to understand the effects of highway work zones, very little definitive information is available concerning the determination of work zone length (WZL). Quantitative studies that holistically model WZL are very rare. To fill this gap, this study identifies critical factors affecting WZL and develops decision support models that determine the optimal WZL in a balanced tradeoff between motorists’ inconvenience due to traffic disruption and their opportunity cost. A high-confidence dataset was created by conducting a series of scheduling and traffic simulations and analyses. The results revealed that traffic loading and work zone duration are critical factors, with traffic loading at approximately 41,000 vehicles-per-day being an important benchmarking point. Based on these findings, a decision support model was developed to determine the most feasible WZL. As the first of its kind, this study will help state transportation agencies devise sounder construction phasing plans by providing a point of reference when establishing WZL in a viable way to minimize traffic disruption during construction

Quantitative Analysis of Warnings in Building Information Modeling (BIM)

Building Information Modeling (BIM) provides automatic detection of design-related errors by issuing warning messages for potential problems related to model elements. However, if not properly managed, the otherwise useful warning feature of BIM can significantly reduce the speed of model processing and increase the size of models. As the first study of its kind, this study proposes to apply the Pareto analysis to investigate BIM warnings in terms of type and frequency. Based on warning data collected from three California healthcare projects, the analysis revealed that the 15-80 rule applies across the case projects and their design phases—15% of the warning messages are responsible for nearly 80% of the warnings. Two other noteworthy findings include: (1) only the schematic design phase indicates a different Pareto rule of 25-80, as well as warning pattern from other design phases due to its unique purpose; and (2) the decisions of individual design teams are a major variable in the pattern of warning types. Lastly, time estimation for warning corrections is proposed based on learning curve theory to support efficient BIM warning management practices. The results and warning classifications presented in this study are expected to contribute to the design management and modeling practices of design teams involved in large, complex projects.Keywords: Building information modeling, Design errors, Design management, Decision making, Pareto analysi

Technical Memorandum on Innovative Contracting Methods Implementation Studies

The main objective of this study is to determine the effectiveness of alternative contracting strategies on aspects of project performance such as schedule and cost, in order to promote ways to apply the strategies effectively. A quantitative analysis that draws on 1,372 infrastructure improvement projects recently completed in California from 2000 to 2008 was conducted to achieve the objective. According to the analysis, alternative projects contracted with I/D and A+B represented 7% of all project establishments and 23% of all project allotment costs. The results of one way ANOVA analyses show that I/D projects held a decisive schedule-saving advantage over A+B and conventional projects, but that I/D also increased project costs significantly more than the others because of a higher frequency of contract change orders. The results of statistical analyses reveal a severe effectiveness problem with use of the A+B contracting strategy. When compared with conventional projects, A+B not only included extreme severe schedule overruns, but it also increased project costs far above the levels seen in conventional projects; both of these resulted from inaccuracies created by allowing contractors to bid on contract time. According to the analysis, the additional cost growth for utilizing I/D was recouped by reduced construction time, but this tradeoff was not seen in A+B projects

Holistic Network-level Assessment of Pavement Flood Damages

After recent catastrophic flood disasters in Louisiana in 2016 and Texas in 2017, roadways in Region 6 areas suffer not only from the flood-inundation, but also from the long-term recovery processes that incur enormous maintenance costs. To assess the impacts of flooding disasters on roadways, various studies have investigated sampled roadway damages with pavement engineering techniques such as a direct damage analysis using cores/bores. However, current methods are time-consuming and labor-intensive. In addition, even though existing methods provide a detailed damage analysis of pavement in a particular location for a particular time period, there is still a large practical knowledge gap in understanding network-level roadway functional/structural damages before-and-after historic flooding as well as assessing flooding impacts on roadways over time. Thus, a holistic perspective and a long-term investigation on roadway damages caused by floods have been rarely addressed, which has resulted in the absence of accurate maintenance cost prediction. The primary objective of this project is to develop a holistic roadway damage assessment method using the flood models and the pavement condition data accumulated over the years. This project also aims to provide a means for Louisiana and Texas (ultimately to all Region 6’s States) to intuitively identify roadway damage patterns at the network level caused by flooding over time as well as predict roadway maintenance tasks. To accomplish the proposed goal, this project examines roadways of parishes and counties in Louisiana and Texas affected by previous flood disasters by using pavement assessment data obtained from the Pavement Management System (PMS) in the Louisiana Department of Transportation and Development (LaDOTD), and the Pavement condition data of the City of Houston. This project is expected to provide a network-level roadway damage assessment and play a pivotal role in reducing the cost of a direct damage analysis such as coring/boring