A number of factors are needed in support of pavement management decisions. This session presents papers ranging from condition assessment to treatment selection.
Effectiveness and Cost-effectiveness Evaluation of Pavement Maintenance Treatments Based on Multiple Regression Analysis and Life-Cycle Cost Analysis
Yuanyuan Pan (firstname.lastname@example.org), Southeast UniversityShow Abstract
Yue Shang, Delft University of Technology
Ye Xing, China Design Group Co., Ltd.
Guoqiang Liu, Southeast University
Anqi Chen, University of Nottingham
Yongli Zhao, Southeast University
With the ever-increasing age and traffic loads, pavements deteriorate fast. Various pavement maintenance and rehabilitation (M&R) activities are adopted to maintain the pavement serviceability, which consumes significant expressway budgets of the project agencies. To optimize the pavement treatment alternatives, the effectiveness and cost-effectiveness of four asphalt pavement maintenance alternatives, including hot in-place recycling, milling and filling, thin HMA overlay and microsurfacing, were quantitatively compared based on the field monitoring data of rutting depth (RD) and maintenance expenditures. Both short- and long-term treatment effectiveness were assessed by the reduction of RD degradation rate (RRDD) and change of average RD (CAR) over the monitoring period, respectively. The influence of the initial RD and annual average daily traffic (AADT) on the treatment effectiveness were analyzed by multiple regression analysis, which indicated the significant role of the factors and also the timing for the treatment. The treatment cost-effectiveness was compared by benefit ratio (B/C) based on life-cycle cost analysis (LCCA), where the hot in-place recycling was found as the most effective and cost-efficient alternative, mainly contributed by the utilization of reclaimed asphalt pavement (RAP). These results provide project agencies with quantitative evidence to support the establishment of the performance-based maintenance decision-making system and the adoption of the RAP in the sustainable pavement management strategies.
Minimizing the Global Warming Potential of Pavement Infrastructure through Reinforcement Learning
Benjamin Corbett, University of British ColumbiaShow Abstract
Sophie Renard, University of British Columbia
Omar Swei (email@example.com), University of British Columbia
Life cycle assessments (LCAs) are frequently used by decision-makers to reduce the environmental burdens of pavement facilities by advising their construction and maintenance policies. Within the pavement life cycle, there are a variety of uncertainties, such as traffic growth and pavement deterioration. Currently, there is a lack of research examining the use of LCA models that can simultaneously optimize construction and maintenance plans while accounting for several sources of uncertainty. This study presents an approach to LCA modelling that implements a sub-type of reinforcement learning (RL) algorithms called Q -learning. Q -learning offers a model-free approach that can efficiently manage stochastic problems of parametric and non-parametric form. The algorithm simulates uncertain values, allowing it to iteratively learn a set of near-optimal decision rules to proactively manage pavement assets for a diverse range of possible future scenarios. These decision-rules are stored in a convenient look-up table, which will appeal to practitioners for its ease of use in probabilistic LCAs. This paper subsequently tests the performance of the Q -learning approach across three representative case studies: a local street-highway, a state highway, and an interstate. The case study results show that, on average, the proposed algorithm reduces the expected global warming potential (GWP) of pavement infrastructure between 13% and 18% over a 50-year analysis period. Based on our results, Q -learning is a promising approach that can help decision-makers account for several sources of uncertainty and implement improved management strategies to mitigate the environmental impacts of their products and systems.
Detection of pavement maintenance treatments using deep-learning network
Lu Gao, University of HoustonShow Abstract
Yao Yu, Independent Scholar
Yi Hao Ren, North Dakota State University
Pan Lu, North Dakota State University
Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models rely heavily on the quality of the collected condition data and M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies due to many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, We employed deep learning networks of Convolutional neural network (CNN), LSTM (Long Short Term Memory), and CNN-LSTM combination to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike the conventional analysis methods so far followed, deep learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.
Solving the Combinational Optimization in Pavement Management Based on Binary Cuckoo Search
Feng Xiao (firstname.lastname@example.org), Southeast UniversityShow Abstract
Shunxin Yang, Southeast University
Jianchuan Cheng, Southeast University
For the optimization analysis in pavement management, combinational optimization is a pervasive problem. Genetic Algorithms (GAs) are widely used to solve combinational optimization problems in pavement management. However, due to the stochastic search mechanisms behind the GA, it is more likely to produce a relatively unsatisfactory solution due to premature convergence. For improving the problem, a binary cuckoo search (BCS) algorithm was implemented to solve the optimization problem and BCS algorithm is the first applied to pavement management, to the best of our knowledge. The effectiveness of BCS algorithm is investigated and demonstrated through three typical hypothetical cases, in which the uncertainty of performance degradation is considered. And the result comparisons between GA and BCS clearly justify the advantages of the search paths behind BCS algorithm in solving the combinational optimization in pavement management.
Use of Time-Temperature Superposition Principle to Create Pavement Performance Mastercurves and Relate PCI and IRI.
Jose Medina, Arizona State UniversityShow Abstract
Akshay Gundla, Arizona State University
Ali Zalghout, GMU Geotechnical Inc.
Samuel Castro, Arizona State University
Kamil Kaloush, Arizona State University
The international roughness index (IRI) is one of the most popular index to measure pavement roughness. State agencies and cities with plenty resources often collect IRI and pavement distresses every year or every other year, but some other with less resources will collect this information every three to five years. Collecting IRI is much more affordable than collecting pavement distresses. With this in mind, the objective of this paper was to establish a relationship between IRI and the pavement condition index (PCI) using pavement deterioration models for both PCI and IRI based on the time-temperature superposition principle, and then combine both models to establish this relationship. Additionally this study was used to establish threshold limits for IRI measurements that can be used as a general reference of pavement condition. Data from the Long Term Pavement Performance InfoPave was used to perform the analysis for three network samples from Arizona, California and Wisconsin. This analysis only included flexible pavements. The results from Arizona, California and Wisconsin showed a good relationship between IRI and PCI using the proposed approach with coefficient of determination ranging from 0.71 to 0.85. Furthermore, the analysis showed that the change in IRI over time can be related to the change in PCI over time. The general thresholds developed in this study applies to the sections evaluated but the approach can be used to set limits for other networks. Keywords: Pavement Condition, IRI, mastercurves, pavement management, LTPP InfoPave
Pavement Management Programs based on Quantitative Performance Targets
MUHAMMAD BEG (email@example.com), AgileAssets, Inc.Show Abstract
ABSTRACT This paper demonstrates network level pavement management programs development for agencies based on maintaining the quantitative performance targets. The goal is to minimize budget demands while maintaining the performance targets. This methodology is unique as it considers the concepts and rules for FHWA MAP-21 and FAST Acts performance target setting for pavements. It shows how agencies can evaluate and define pavement performance measure targets while establishing their network level work programs. This paper reports the results of a case study for a sample roadway network. Study approach was based on establishing multiple performance targets based on key performance indicators: IRI, Rutting and %Cracking. Establishing multiple candidate scenarios including different intervention strategies to accomplish agency network performance targets while developing optimal pavement management programs. Scenarios include two approaches: 1) minimizing the budget for given fixed performance targets for IRI, Rutting, and Cracking, 2) maximizing the performance given the fixed budget levels. Then developed ten-year pavement management programs for the network for each scenario. The results were compared for the costs for each scenario and the performance targets maintained for each scenario. The methodology utilizes the multi-constraint optimization analyses to evaluate each network level analysis scenario while satisfying performance constraints. The case study demonstrates that scenarios with focus on preservation can help reduce the costs of meeting performance targets. Keywords: Asset Management, Pavement Management, Performance Targets, MAP-21, FAST, Performance Management
A Framework for Integrated Infrastructure Condition Assessment and Traffic Assignment
Mirla Abi Aad, Virginia Polytechnic Institute and State University (Virginia Tech)Show Abstract
Montasir Abbas, Virginia Polytechnic Institute and State University (Virginia Tech)
The Virginia Department of Transportation (VDOT) is responsible for more than 128,000 lane miles of roadway that need to be in a good state of repair. Pavement condition data are crucial in estimating the cost to achieve and sustain VDOT's pavement performance targets. VDOT also strives to reduce congestion on its road networks, implementing different traffic operation, control, and travel demand management strategies. While these strategies result in changes to travel demand distributions, road congestions, travel routes, and travel modes, and while the condition of the pavement is obviously related to the traffic loading, there is currently no decision-support tool that can be used to predict the impact of these different strategies on pavement conditions. This paper presents the first steps in implementing a framework of integrated pavement condition assessment and traffic assignment that is intended to be included in the final project decision-support tool. The authors illustrate the proposed framework and provide a proof-of-concept study in Northern Virginia. An optimization process is involved in selecting pavement treatment and a dynamic profiler is used to show the interaction of traffic volume and overall budget in the final performance measures.
Segmentation of Highway Networks for Maintenance Operations
Moo Yeon Kim (firstname.lastname@example.org), University of Texas, AustinShow Abstract
Jorge Prozzi, University of Texas, Austin
Moo Yeon Kim (email@example.com), University of Texas, Austin
Moo Yeon Kim (firstname.lastname@example.org), University of Texas, Austin
Pavement maintenance and rehabilitation (M&R) activities are essential for transportation agencies to manage a sustainable transportation infrastructure efficiently. In maintenance operations, obtaining the limits of homogeneous sections is a crucial problem because appropriate segmentation helps to yield a more cost-effective M&R plan. The purpose of this study was to investigate previous research studies in this area and to establish recommendations for developing an enhanced methodological framework. Existing approaches for pavement segmentation were explored through a literature review. Data analyses using change-point detection approaches available in a statistical program R were performed using pavement condition data. Future work directions were suggested to develop a segmentation method capable of handling the issues found in the study.
DISCLAIMER: All information shared in the TRB Annual Meeting Online Program is subject to change without notice. Changes, if necessary, will be updated in the Online Program and this page is the final authority on schedule information.