Predicting asphalt pavement crack initiation following rehabilitation treatments

Prolongation of the service life of pavements requires efficient prediction of the performance of their structural condition and particularly the occurrence and propagation of cracking of the asphalt layer. Although pavement performance prediction has been extensively investigated in the past, models for predicting the cracking probability and for quantifying impacts of associated explanatory factors following pavement treatment, have not been adequately investigated in the past. In this paper the probability of alligator crack initiation following pavement treatments is modeled with the use of genetically optimized Neural Networks, The proposed methodological approach represents the actual (observed) relationships between of probability of crack initiation and the various design, traffic and weather factors as well as the different rehabilitation strategies. Data from the Long Term Pavement Performance (LTPP) Data Base and the Specific Pavement Study 5 (SPS-5) are used for model development. Results indicate that the proposed approach results in accurately predicting the probability of crack initiation following treatment; furthermore it provided information on the relationship between external factors and cracking probability that can help pavement managers in developing appropriate rehabilitation strategies.     

Impact of climate change on pavement structural performance in the United States

This study uses climate projections from multiple models and for different climate regions to investigate how climate change may impact the transportation infrastructure in the United States. Climate data from both an ensemble of 19 different climate models at both RCP8.5 and RCP4.5 as well as three individual prediction models at the same Representative Concentration Pathways (RCP) levels is used. These models are integrated into the AASHTOWare Pavement ME software to predict the pavement performance. Comparisons are made between the predicted performance with respect to typical pavement distresses using both historical climate data as well as climate projection data. Though there is substantial variation for different prediction models in terms of the magnitude of the impact, the consistency in results suggest that projected climate changes are highly likely to result in greater distresses and/or earlier failure of the pavement. This finding is consistent across all the climate zones studied, but varies in magnitude of 2–9% for fatigue cracking and 9–40% for AC rutting at the end of 20 years depending on the climate region of the pavement section and prediction model used. This study also compares the impacts incorporating temperature only projections with temperature and precipitation projections. In this respect, the sections considered in this study do not show any substantial difference in the pavement performance when the precipitation data from the climate predictions are also considered in the climate inputs into AASHTOWare Pavement ME software.     

Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

Pavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness‐of‐fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system. Copyright © 2010 John Wiley & Sons, Ltd.     

时间序列法预测沥青路面损坏状况指数研究

由于沥青路面损坏状况影响因素很多,因此要准确预测路面损坏状况较困难。文章采用时间序列法建立预测模型,结合同三高速公路（上海段）路面损坏状况的实测数据进行预测分析。分析结果表明时间序列法具有较高的预测精度和易修正性。     

A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.     

不同沥青路面结构抵抗车辙性能理论计算分析

车辙是沥青路面早期损坏主要形式,特别在陡坡及超载的条件下,沥青路面车辙病害更为突出。文章拟定了5种沥青路面结构组成,建立有限元模型计算了各种沥青路面结构的力学参数,对各种路面结构抵抗车辙的性能进行了分析对比。     

南方地区冰冻灾害后沥青路面典型病害发展原因探讨

为研究冰冻灾害后路面病害加剧发展的灾变机理,文章从冰冻灾害期间及灾后气候因素变化入手,分析了冰灾前后不利的气候因素与路面病害之间的相关性,并结合路面病害调查与芯样破坏特征揭示了冰灾期间及后期路面病害发展加剧的原因与扩展过程。     

Incorporating socio-political criteria into the maintenance prioritization of Chilean urban pavement networks

Managing urban pavement networks presents additional challenges when compared to the management of interurban pavements. In particular, the prioritization of maintenance activities – which is critical when resources are limited – requires special considerations. Within these considerations, there are socio-political criteria that are not formally considered in current UPMS (Urban Pavement Management Systems). In practice, decision makers consider these socio-political factors but without a formal procedure and proper information, leading to decisions based on subjective information, which lack traceability and reliability.The objective of this study is the identification and formal definition – including the quantification method – of socio-political criteria relevant for the sustainable management of urban pavement networks. The research method included the application of interviews and a survey of experts and practitioners in various agencies involved in the pavement maintenance decision-making process in Chile. As a result of the study, five primary socio-political criteria were identified: neighbors’ perception, proximity to critical infrastructure, benefited population, presence of alternative routes, and strategic selection based on public policy. These criteria were formalized – including how they should be quantified – through an expert panel. A regression analysis applied to various scenarios considered in the survey resulted in the quantification of the relative importance of the formalized socio-political factors to be considered in the decision process, complementing technical and economic criteria. Future research will explore the use of Geographic Information Systems (GIS) to quantify the recommended socio-political factors and implement them in an UPMS.     

广西复合式路面方案设计初探

文章探讨的复合式路面是一种优势互补、发挥地方资源优势的发展潜力巨大的路面结构形式。通过计算分析所得的三种复合式路面结构方案,均考虑了车辆的超、重载问题,可较好的适应目前重载交通的需要,对进行复合式路面的结构设计具有一定的参考价值。     

Joint optimization of freight facility location and pavement infrastructure rehabilitation under network traffic equilibrium

Establishment of industry facilities often induces heavy vehicle traffic that exacerbates congestion and pavement deterioration in the neighboring highway network. While planning facility locations and land use developments, it is important to take into account the routing of freight vehicles, the impact on public traffic, as well as the planning of pavement rehabilitation. This paper presents an integrated facility location model that simultaneously considers traffic routing under congestion and pavement rehabilitation under deterioration. The objective is to minimize the total cost due to facility investment, transportation cost including traffic delay, and pavement life-cycle costs. Building upon analytical results on optimal pavement rehabilitation, the problem is formulated into a bi-level mixed-integer non-linear program (MINLP), with facility location, freight shipment routing and pavement rehabilitation decisions in the upper level and traffic equilibrium in the lower level. This problem is then reformulated into an equivalent single-level MINLP based on Karush–Kuhn–Tucker (KKT) conditions and approximation by piece-wise linear functions. Numerical experiments on hypothetical and empirical network examples are conducted to show performance of the proposed algorithm and to draw managerial insights.     

沥青路面动力响应分析方法研究

西部交通建设科技项目"沥青路面动力响应分析方法研究"旨在建立车辆-道路系统动力学模型,研究高速重载车辆荷载作用下沥青路面结构的动力响应情况,分析动力载荷对沥青路面结构使用性能的影响,从而提出适合重载交通条件的沥青路面动力响应理论分析方法。本篇对该项目的技术创新点、主要研究成果、社会经济效益及推广应用前景进行了介绍,为今后沥青路面的结构改造与设计提供理论依据。     

Multi-objective optimization for asphalt pavement maintenance plans at project level: Integrating performance,cost and environment

Traditionally, asphalt pavement maintenance mainly considers pavement performance and cost and largely ignores the environment while substantial amount of environmental burdens are released in the process. In this study, a multi-objective optimization model was developed integrating the three elements in order to optimize the asphalt pavement maintenance plans at the project level. Pavement performance element was decided as the multiplier of pavement serviceability index and traffic volume. Cost element was represented by the net present value, including components of agency cost, vehicle operation cost and salvage value. Environmental element, integrating energy consumption, global warming potential, acidification potential and respiratory effects potential, was measured by the life cycle assessment model. A hypothetic asphalt pavement maintenance case study was conducted using the developed multi-objective optimization model and harvested 103 sets of feasible combinations of maintenance plans, each of which is non-dominated by the others. Trade-offs analysis was performed among the three objectives and visualized in both two- and three-dimension forms. It is found there is an opportunity of reducing the cost and environmental impacts to 80.3% and 77.8% and increasing the pavement performance to 146.6% compared to the base case. However, they are mutually compromised and cannot be reached simultaneously. The developed model reveals the quantitatively interactive relationship of the three objectives and helps optimize the asphalt pavement maintenance plans.     

Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions

This research intends to explore external factors affecting driving safety and fuel consumption, and build a risk and fuel consumption prediction model for individual drivers based on natural driving data. Based on 120 taxi drivers’ natural driving data during 4 months, driving behavior data under various conditions of the roadway, traffic, weather, and time of day are extracted. The driver's fuel consumption is directly collected by the on-board diagnostics (OBD) unit, and safety index is calculated based on Data Threshold Violations (DTV) and Phase Plane Analysis with Limits (PPAL) considering speed, longitudinal and lateral acceleration. By using a linear mixed model explaining the fixed effect of the external conditions and the random effect of the driver, the influences of various external factors on fuel consumption and safety are analyzed and discussed. The prediction model lays a foundation for drivers' fuel consumption and risk prediction in different external conditions, which could help improve individual driving behavior for the benefit of both fuel consumption and safety.     

Modelling of the pavement acoustic longevity in Hong Kong through machine learning techniques

Traffic noise emission has long been a pervasive environmental and ecological problem, especially in the metropolitan cities with large-scale traffic network and high population density. Low noise road surface (LNRS) has been actively developed and applied as an effective measure to maintain the quieter environment of mobility service system. However, when LNRS is applied for noise abatement, the relationship between the acoustic performance and degradation of pavement has not been fully understood yet. To this end, this study aims to model the acoustic longevity of asphalt pavement as a function of the thickness, binder content, maximum aggregate size, and air void content of the pavement surface, as well as vehicle speed based on the long-term tyre-road noise data collected from 270 asphalt pavement sections in Hong Kong. Two machine learning techniques, namely artificial neural networks (ANN) and support vector machines (SVM), were employed and compared. It was found that both ANN and SVM could successfully model the pavement acoustic performance with acceptable model performance metrics. A case study showed that the ANN model was more aligned with the aging mechanisms of porous road surface, but the SVM model showed better training performance. The predicted acoustic deterioration rates of the porous surface case varied from −0.1 to 0.28 dB(A)/month rather than keeping a constant linear increasing trend, depending on pavement ageing periods and vehicle speed levels. The two-dimension sensitivity analysis (2D-SA) revealed the relative importance of pavement age and vehicle speed in controlling the acoustic performance.     

Estimating vehicle speed with embedded inertial sensors

Pavements were instrumented with inertial sensors, and the possibility of estimating the speed of a passing vehicle was investigated numerically and experimentally from the measurements of two embedded accelerometers. The sensors were spaced apart in the travel direction, and subsequently the speed was directly related to the time delay between the received signals. No assumption was made regarding the vehicle and pavement properties. Model accelerations were presented, studied, and contrasted against field measurements; the latter were shown to be dominated by random vibration sources. Two calculation techniques were offered and applied to handle the noisy data. The first was based on time-centroids, and the second was based on cross-correlation with kernel presmoothing. The overall concept is deemed promising not only for inferring speeds but also for extracting additional traffic characteristics such as axle spacing and relative axle load distributions.     

A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction

We present a methodology for the joint optimization of rehabilitation and reconstruction activities for heterogeneous pavement systems under multiple budget constraints. The proposed bottom-up approach adopts an augmented condition state to account for the history-dependent properties of pavement deterioration, and solves for steady-state policies for an infinite horizon. Genetic algorithms (GAs) are implemented in the system-level optimization based on segment-specific optimization results. The complexity of the proposed algorithm is polynomial in the size of the system and the policy-related parameters. We provide graphical presentations of the optimal solutions for various budget situations. As a case study, a subset of California’s highway system is analyzed. The case study results demonstrate the economic benefit of a combined rehabilitation and reconstruction budget compared to separate budgets.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

道路透水路面不同面层材料的特性探讨

我国新近应用的透水路面具有很好的应用前景.文章从工程应用角度分析透水沥青混合料、透水水泥混凝土、透水路面砖等三种透水性面层材料的强度特性与耐候性,探讨其常见病害与维护、维修方法.     

Evaluating the effects of climate change on road maintenance intervention strategies and Life-Cycle Costs

Climate change has the potential to impact long-term road pavement performance. Consequently, to maintain pavements within the same ranges of serviceability as before, current pavement maintenance strategies need to be re-assessed and, if necessary, changed. Changes in maintenance may lead to different agency costs and user costs as a consequence. This paper commences by defining an assessment procedure, showing how maintenance intervention strategies and Life-Cycle Costs (LCC) may be affected by future climate. A typical Virginia flexible pavement structure and anticipated climate change was used as an example. This example is believed to be representative for a great number of localities in the United States. A method using historical climatic data and climate change projections to predict pavement performance using Mechanistic-Empirical Pavement Design Guide (MEPDG) under current or future climate was introduced. Based on pavement performance prediction, maintenance interventions were planned and optimized. The maintenance effects of three treatments (thin overlay, thin overlay with an intermediate layer, and mill & fill) were considered. A Life-Cycle Cost analysis is reported that used binary non-linear programming to minimize the costs (either agency costs or total costs) by optimizing intervention strategies in terms of type and application time. By these means, the differences in maintenance planning and LCC under current and future climate can be derived. It was found, that for this simplified case study, pavement maintenance and LCC may be affected by climate change Optimized maintenance may improve resilience to climate change in terms of intervention strategy and LCC, compared to responsive maintenance.     

A network level connectivity robustness measure for connected vehicle environments

This study introduces a new CONnectivity ROBustness model (CONROB) to assess vehicle-to-vehicle communication in connected vehicle (CV) environments. CONROB is based on Newton’s universal law of gravitation and accounts for multiple factors affecting the connectivity in CV environments such as market penetration, wireless transmission range, spatial distribution of vehicles relative to each other, the spatial propagation of the wireless signal, and traffic density. The proposed methodology for the connectivity robustness calculation in CONROB accounts for the Link Expiration Time (LET) and the Route Expiration Time (RET) that are reflected in the stability of links between each two adjacent vehicles and the expiration time of communication routes between vehicles. Using a 117 sq-km (45-square mile) network in Washington County, located west of Portland city, Oregon, a microscopic simulation model (VISSIM) was built to verify CONROB model. A total of 45 scenarios were simulated for different traffic densities generated from five different traffic demand levels, three levels of market penetration (5%, 15%, and 25%), and three transmission range values [76 (250), 152 (500), and 305 (1000) m (ft)]. The simulation results show that the overall robustness increases as the market penetration increases, given the same transmission range, and relative traffic density. Similarly, the overall connectivity robustness increases as the relative traffic density increases for the same market penetration. More so, the connectivity robustness becomes more sensitive to the relative traffic density at higher values of transmission range and market penetration. Multiple regression analysis was conducted to show the significant effect of relative traffic density, transmission range, and market penetration on the robustness measure. The results of the study provide an evidence of the ability of the model to capture the effect of the different factors on the connectivity between vehicles, which provides a viable tool for assessing CV environments.