Pavement Rehabilitation Strategies Research Articles

A Comprehensive Review of Life Cycle Cost Assessment of Recycled Materials in Asphalt Pavements Rehabilitation

This paper provides a comprehensive review of the use of life cycle cost assessment (LCCA) and life cycle assessment (LCA) methods for evaluating the sustainability and costs of using recycled materials in asphalt pavement rehabilitation projects. The review begins with an introduction to pavement rehabilitation strategies and the importance of choosing techniques based on thorough engineering and economic analyses. It then explores the different types of recycled materials that can be utilized, including reclaimed asphalt pavement, recycled concrete aggregate, and recycled asphalt shingles, discussing the key characteristics and properties of these materials based on previous laboratory studies. The review also examines the various rehabilitation methods that employ recycled content, such as cold in-place recycling, hot in-place recycling, and full-depth reclamation, providing a detailed breakdown of the construction, maintenance, and rehabilitation costs considered in LCCA and analyzing the environmental benefits of recycled material usage through a review of LCA techniques and criteria like carbon footprint reduction, impacts on air and water quality, and considerations of technological factors. Software tools for conducting LCA are compared and challenges to advancing the adoption of recycled materials are reviewed along with directions for future research efforts. The unique contribution of this work is its holistic assessment of LCCA and LCA methodologies to inform the sustainable and cost-effective deployment of recycled materials in asphalt pavement rehabilitation, a topic of growing importance for transportation infrastructure management. In summary, this current work provides a valuable review of how LCCA and LCA methodologies can assess the sustainability and costs of employing recycled content in asphalt pavement rehabilitation projects.

Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.

Machine-Learning-Based Framework for Prediction of the Long-Term Field Performance of Asphalt Concrete Overlays in a Hot and Humid Climate

Pavement performance prediction models are used by state agencies to determine pavement maintenance and rehabilitation strategies. However, most performance prediction models are based on a limited number of parameters and a maximum prediction period of five years. With the ever-increasing amount of available pavement performance data, machine-learning techniques have become a promising alternative to traditional performance prediction models. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (up to 11 years) of asphalt concrete (AC) overlays on asphalt pavements based on key project conditions. The pavement condition index (PCI) was used as the pavement performance indicator. Two machine-learning algorithms, namely, random forest (RF) and CatBoost, were examined. A total of 892 log-miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development Pavement Management System database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the PCI of AC overlays for up to 11 years. The results indicated that the RF algorithm yielded higher accuracy than the CatBoost algorithm. Therefore, the RF-based models were considered in the proposed decision-making framework.

Use of Pavement–Vehicle Interaction-Related Models to Estimate Excess Fuel Consumption of Pavement Alternatives During the Design Stage

The need to quantify the effect of various pavement characteristics on pavement–vehicle interactions (PVI) and the associated excess fuel consumption (EFC) has been identified by practioners of pavement life cycle assessment (LCA), particularly for the use phase. With the current need to reduce carbon emissions, if found to be significant, these effects might also need to be considered by agencies when making investment decisions or when evaluating pavement design and rehabilitation strategies. Several studies have evaluated rolling resistance factors such as pavement roughness, macrotexture, and structural response (SR) to loading to generate models to predict EFC. Available PVI models consider either the effects of pavement surface characteristics (PSC) or SR only, and it is not possible to estimate the total PVI-related EFC using just one model. This paper summarizes a study demonstrating the use of select PVI models for estimating EFC during the pavement design stage for a new construction or reconstruction project and attempts to combine the results of PSC models with SR models to calculate the total contribution of PVI to EFC. The results from the analysis may be helpful for evaluating alternative pavement design strategies, materials, or surface textures for inclusion in the final design and project specifications, provided the gaps and limitations of the models are well understood and considered. The study also identified challenges that might be encountered by agencies in performing such analyses, such as availability of high-quality data for longer analysis periods and lack of measurement methods for ground truth.

Life-Cycle Cost Analysis on Application of Asphalt and Concrete Pavement Overlay

Concrete pavement proportions are increasing in Korean expressways, resulting in increased maintenance cost. The length of degenerate concrete pavements that have exceeded the design life (20 years) was 1150 km in 2015 and 2605 km in 2020 and is expected to rapidly increase. To extend the service life of concrete pavements, life-cycle cost (LCC) analysis was conducted on asphalt and concrete overlays, based on the different maintenance methods. LCC analysis was performed when the shoulder was used and when it was not used between 6000 and 35,000 AADT traffic according to the two-lane and four-lane traffic. During overlay, one lane was completely blocked, and the value per vehicle was converted into the user cost using the Construction Analysis for Pavement Rehabilitation Strategies software, according to whether the shoulder was used to maintain the number of lanes. In addition, LCC analysis was conducted by examining the construction cost and life-cycle according to each overlay method. When the shoulder was used, the total construction cost decreased, owing to the reduction in user cost, indicating that the implementation of the traffic measure of using the road shoulder improves user satisfaction and cost. The asphalt overlay was observed to be more favorable than concrete overlay in terms of the initial total construction cost. However, under a 20-year cycle, the economic efficiency of concrete overlay was higher than that of asphalt overlay. After repairing the deteriorated target sections of concrete pavements, the overlay method (asphalt or concrete) ought to be selected according to the target service life for beneficial economic effect. Concrete overlay was to obtain about 20% or greater LCC effect compared to asphalt overlay, and at least 5% or more additional LCC effect obtained when the shoulder was used.

Impacts of future climate change on flexible road pavement economics: A life cycle costs analysis of 24 case studies across the United States

• A methodological framework to analyze the impacts of climate change on pavement LCC is presented • Pavement LCC are estimated to increase 0.08-0.21% per °C increase in future temperature • Pavement LCC were found to be particularly sensitive to gasoline and diesel unit costs Highway agencies are facing pressure for repairing and replacing underfunded and aged road pavement networks that were initially designed for historical climatic conditions. Road pavements must be updated while accounting for the impacts of climate change, which are likely to be exacerbated in the years to come, and with limited budgets. Methodologies and frameworks that help agencies incorporate the long-term effects of climate change on pavement performance and make informed decisions on how to spend their limited funds are therefore of utmost importance. This paper presents a methodological framework that combines downscaled climate projections, pavement performance predictions using the AASHTOWare Pavement ME Design TM tool, maintenance and rehabilitation strategies, and life cycle costs analysis (LCCA) in a comprehensive system. The proposed methodological framework was adopted in the LCCA of 24 case studies across the contiguous United States under four alternative periods corresponding to simulated climate projections for four 20-year periods (1981-2000, 2001-2020, 2041-2060, and 2081-2100) with a higher Representative Concentration Pathway (RCP8.5). The case study results show that climate change per degree Celsius will lead to approximately $650-700 million/year additional agency costs in the U.S. In addition, climate change will have greater impacts on the costs incurred during the maintenance and end-of-life phases.

A Comparative Study of Probabilistic and Deterministic Methods for the Direct and Indirect Costs in Life-Cycle Cost Analysis for Airport Pavements

Airports play a critical role in transporting goods and passengers and supporting the growth of the world economy. Airports spend huge sums annually to maintain and improve pavement functions by expanding the runways, taxiways, and aprons, and perform routine maintenance and rehabilitation of the existing pavements. Besides the traditional direct costs, a comprehensive airport pavement management system should also consider indirect costs such as fuel, crew, passenger delay, aircraft maintenance, and loss of airport revenue when conducting a life-cycle cost analysis (LCCA). Engineers, managers, and stakeholders can make better decisions on the appropriate pavement maintenance and rehabilitation strategies by performing economic analyses of the direct and indirect costs. This study performed probabilistic and deterministic LCCA to contrast the effect of direct costs vis-a-vis indirect costs in airport pavement management. A case study found that indirect costs could contribute up to 20% of the total costs when using Portland cement concrete (PCC), hot mixed asphalt (HMA), and crack seat overlay (CSOL). Previous research did not give much attention to maintenance since the researchers believed that routine maintenance makes up only an insignificant percentage of the LCCA. However, routine maintenance of HMA and CSOL makes up 10.2% and 14.2% of the total cost. The rehabilitation cost of PCC makes up 16.3% of the total cost, and the rehabilitation cost for HMA and CSOL makes up 25.4% and 35.2% of the total cost.

Impact analysis of traffic loading on pavement performance using support vector regression model

ABSTRACT This study aims to use traditional regression model and machine learning method to analyse the impact of traffic loading on pavement performance. Pavement condition data were obtained from pavement management systems (PMS) and axle loads of truck traffic were collected at weigh-in-motion (WIM) stations. Support vector regression (SVR) method was selected for modelling pavement performance since it provides the flexibility to find the appropriate hyperplane in higher dimensions to fit the data and customise control errors in an acceptable range. Compared to traditional nonlinear regression model, the accuracy of pavement performance prediction was significantly increased by utilising the SVR method. The model accuracy was further improved by considering the number of axles and fitted Gaussian distribution of axle load spectra in the performance model. The derived SVR models were further used to investigate the impact of overweight truck on pavement life reduction considering characteristics of axle load distributions. The proposed pavement performance model can be further used in determining pavement damage caused by overweight trucks for pavement rehabilitation strategy and fee analysis is permitted.

Life-Cycle Cost Analysis (LCCA) comparison of pavements (Flexible, rigid and rigid-admixed with cow bone ASH)

Life Cycle Cost Analysis (LCCA) acts as a decision support tool in economic evaluation of cost (agency and user) during pavement type selection, maintenance and rehabilitation strategy. The Life cycle cost analysis was done using the Present worth of Cost method. Technical Recommendations for Highway (TRH) 12 (pavement rehabilitation investigation and design) analysis was used for calculating the agency cost which entailed the initial rehabilitation, maintenance, future and salvage cost. The LCCA analysis period for this study was taken as 40 years as the analysis period have to be sufficiently long to reflect long-term cost differences associated with reasonable design strategies. The result of the study shows that the present worth cost for the varying Pavement presents the options available for decision making. The result revealed that the initial cost of Rigid pavement is the highest followed by the initial cost of Rigid pavement with 15% CBA while flexible Pavement has the lowest initial cost. However, considering the result showing the present worth cost for the varying pavement types present worth cost of flexible pavement is the highest followed by Rigid pavement and Rigid pavement with 15% CBA has the lowest life cycle cost. The study recommended that Rigid pavement with 15% CBA should be considered because it gives the lowest life cycle cost and the initial cost is relatively low.

Permanent deformation analysis of base layers with recycled material: Effect of density and stress level

Full-depth reclamation techniques present numerous advantages for pavement rehabilitation strategies. The recycling process produces a thick layer of stabilized or unstabilized reclaimed materials, which is used as a new base layer of RAP aggregates. The compaction of the unstabilized thick layer is critical for adequate permanent deformation behaviour of the reclaimed layer. In some agencies, the smooth drum compaction is often used directly after the recycling process to compact the reclaimed materials. Previous studies demonstrated the challenges associated with adequate densification of the thick reclaimed layer through its whole thickness, leaving among other a material with a vertical density gradient. In that context, this study was designed to quantify the effect of the degree of compaction on the permanent deformation behaviour using cyclic triaxial tests. Granite and limestone aggregates were mixed with RAP in order to obtain mixes with 50% RAP, and each blend was tested using a permanent deformation test procedure at four different degree of compaction. One of the tested degree of compaction was a varying compacted density over the sample height. It was found that the analysis of this particular sample performance could be done on the basis of the average dry density of the sample. The shakedown ranges of each sample were analyzed with respect to stress ratio and degree of compaction. The behaviour for each type of mix was also similar. A generalized model was therefore proposed allowing to predict the range of permanent deformation behaviour knowing stress level and the degree of compaction. The general model between shakedown range and a relative stress ratio is based on a shift factor linking the effect of stress and density. Limit states were also defined for the possible ranges of behaviour with respect to the degree of compaction.

Long-term performance evaluation of Iowa concrete overlays

ABSTRACT The use of concrete overlays has long been recognised as a cost-effective pavement maintenance and rehabilitation strategy. However, the long-term performance of various types of concrete overlays has not been fully investigated since there has not been enough performance data available to conduct such an evaluation. Concrete overlays have been regularly constructed on Iowa roadways since the late 1970s and many older projects are still in use. Performance-related data for in-service concrete overlays have been acquired from the available resources to evaluate long-term performance of concrete overlays in Iowa. The information collected includes Pavement Condition Index (PCI), International Roughness Index (IRI), overlay type, construction year, overlay thickness, joint spacing, traffic, and other construction and design-related data. Based on an evaluation of PCI and IRI changes during service life, it is observed that concrete overlays can provide at least 20 years of service life. In terms of PCI ratings, 89% of concrete overlays investigated have PCI values greater than 60% as of the time of the analysis. Similarly, 93% of concrete overlays have IRI values lower than 2.7 m/km (170 in/mile). The effects of overlays type and design features on long-term performance of Iowa concrete overlays are also discussed.

Mitigating the legal challenges of imposing disincentives on nonperforming transportation projects

PurposeThe publication presents an analysis of the cost and schedule performance of incentive/disincentive projects and case studies toward developing a systematic disincentive valuation process, with Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) software integration that aids agencies in minimizing the likelihood of court challenges of disincentives.Design/methodology/approachFrom a California transportation database, the authors performed cost and schedule analyses of 43 incentive/disincentive (I/D) projects and case studies on four of those I/D projects. Interviewees included subject matter experts from transportation organizations to ensure applicability and maximum value-adding, and the process was implemented on ten California transportation projects and monitored for performance.FindingsThe presented process mitigates the contractor's ability to claim disincentives as penalties in a court of law through the following: (1) all calculations are performed using project-specific bases, backed by estimations of actual incurred costs; (2) the CA4PRS software allows for estimation transparency and (3) the clarity of cost inclusions reduces any chances of “double-dipping” between disincentives and liquidated damages.Practical implicationsTransportation agencies have historically faced legal challenges to their enforcements of disincentives. As agencies continue to apply disincentives on more megaprojects, contractors will likely attempt to pursue legal challenges more frequently. The presented process mitigates the likelihood of these challenges going to court and increases the accuracy and efficiency of disincentives.Originality/valueWhile there have been publications that discuss the legal challenges of imposing disincentives, they mainly provide guidelines and lack applicable processes. Existing literature that does present incentive/disincentive valuation process focuses on incentive valuations and neglects the disincentives' legal challenges. The following publication fills this gap by presenting an applicable disincentive valuation process for transportation projects which incorporates the guidelines for legal mitigation.

INTEGRATING CA4PRS V.3 ROAD WIDENING SCHEDULE MODULE INTO US HIGHWAY EARLY CONSTRUCTABILITY PROCESS: CALIFORNIA SR-91 CORRIDOR IMPROVEMENT PROGRAM CASE STUDY

Performing Constructability Review Processes (CRPs) during the highway design development has been found to save transportation agencies twice their input costs. However, existing literature has identified three areas of CRP improvement: reduction of required agency resources, incorporation of Road User Cost (RUC) scheduling constraints, and integration of assessment visualizations. The authors propose to fill this gap by integrating the Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) v.3 software into the CRP. This module provides agencies with road widening project schedule capabilities, which enhances CRPs by providing accurate RUC-constrained critical path schedules using minimal resources. The module was developed through interviews with subject matter experts from six public and two private California transportation organizations. Said experts also tested the CA4PRS v.3 alpha and beta pre-release versions using data collected from eight Caltrans road widening projects. The potential value-adding of integrating the CA4PRS v.3 software with existing CRPs has been tested through its application on the California State Road 91 (SR-91) Corridor Improvement Program (CIP), resulting in 24-months of construction acceleration. The findings and presentation of the schedule model within this paper provide practitioners an accurate and resource-efficient tool to estimate the schedule impacts of road widening constructability options.

Distress and profile data analysis for condition assessment in pavement management systems

Pavement data collection is the most expensive and time consuming component of Pavement Management System (PMS). Thus, possible methods of minimizing the need of such data might be critical in reducing pavement condition monitoring costs. Also the ability to relate pavement performance prediction models (frequently roughness based) to hot mix asphalt field performance models (distress based) provides valuable conclusions and input in pavement design, performance assessment, maintenance and rehabilitation strategies. Objective of this study was to examine whether specific distresses can influence roadway profile so as to be able to relate the two. The influence of pavement distresses on road profile has been investigated over the years. However, past studies provided conflicting conclusions. Thus, in this study an alternative approach was considered due to the availability of high quality and detailed distress data collected with the Laser Crack Measurement System (LCMS) of the Automatic Road Analyzer, ARAN. As it was expected, specific distresses have higher impact in longitudinal roughness since they are present on the roadway surface at regular intervals (i.e., specific frequencies). For this reason, instead of using summary indexes (i.e., International Roughness Index (IRI), Pavement Condition Index (PCI)), the Power Spectral Density (PSD) of the roadway profile at specific frequency bandwidths was considered along with distresses. The analysis indicated that a specific subset of distresses is affecting roughness at definite wavelength frequencies. Alligator cracking and rutting standard deviation provided the best correlation. IRI was correlated better with distress (e.g. rutting standard deviation) at lower profile frequencies. At high frequency domain (i.e., below 0.8 m wavelengths) better correlation between IRI and high severity cracking was observed through the PSD. Considering multiple frequencies in the regression models between roughness and distresses, the goodness of fit has not necessarily improved. However, the role of different bandwidths was evident. In addition to the specific results, the methodology presented in this study can be used elsewhere to assess potential relations between pavement roughness and distress components.

Field evaluation of porous asphalt course for life-cycle cost analysis

Porous asphalt courses (PAC) provide environmental and safety benefits by reducing traffic noise and improving drainability. As pressure increases on the cost and availability of natural resources, a greater understanding is needed of field performance to allow maintenance and preservation of resilient, sustainable and economic networks. The main purpose of this paper is to utilize life-cycle cost analysis (LCCA) as a decision-support tool for three pavement rehabilitation strategies. These alternatives included open-graded friction course using natural aggregate (OGFC), porous asphalt course using natural aggregate (PAC), and porous asphalt course using steel slag (SS-PAC). The LCCA followed the Federal Highway Administration’s RealCost procedure and incorporated information collected from highway agencies. Based on the results of the performance analysis, pavement life-cycle models were developed for each alternative. Using the life-cycle models and historical unit costs, deterministic and probabilistic LCCAs were conducted. The results demonstrated the cost saving of SS-PAC in both low and medium price ranges. In order to have economic benefits, the service life of SS-PAC needed to be more than eight years for a high price range. SS-PAC had a 78 and 92 percent chance of being more cost-effective than OGFC and PAC according statistical analysis, respectively. Conclusions from the LCCA supported use of the SS-PAC alternative since SS-PAC was shown to be relatively cost-effective over the 40-year analysis period. Utilization of the SS-PAC alternative would combine cost efficiency and sustainability for highway projects, especially rehabilitation and reconstruction.

Soft Computing Models to Predict Pavement Roughness: A Comparative Study

Pavement roughness as a critical determinant of public satisfaction can potentially play a major role in road or highway resource allocation to competing pavement resurfacing projects. With this in mind, the aim of the present paper is to develop an accurate model for the prediction of pavement roughness in terms of the International Roughness Index (IRI) using artificial neural networks (ANNs) and support vector machines (SVMs). The modeling is based on pavement roughness data collected periodically for a high-volume motorway during a seven-year period, on a yearly basis. The comparative study of the developed models concludes that the performance of the ANN model is slightly better compared to the SVM in terms of prediction accuracy. Further, the analysis results produce evidence in support of the statement that both models are capable to predict accurately pavement roughness; hence, they are deemed useful for supporting decision making of pavement maintenance and rehabilitation strategies.

Incorporating Road User Costs into Integrated Life-Cycle Cost Analyses for Infrastructure Sustainability: A Case Study on Sr-91 Corridor Improvement Project (Ca)

Life-cycle cost analysis (LCCA) is a decision-making tool that allows governing agencies the ability to assess several long-term alternative investment options. This paper presents a LCCA analysis process which integrates the Federal Highway Administration (FHWA) program, RealCost (a road user cost calculation program), the FHWA-endorsed Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) and Caltrans specific design tools (CalFP and CalAC), into the existing Caltrans LCCA process (a modified version of the FHWA LCCA process). In using tools backed by the FHWA and validated through previous agency use, the presented process has a potential to be replicated on urban corridor improvement projects across the US while aiding agencies in achieving economical sustainability throughout the infrastructure maintenance phases. This paper also fills the gap identified by Ozbay et al. in 2004, incorporating road user cost calculations into the LCCA process. Validation was achieved through the execution of the recently completed $1.4 B US California SR-91 Corridor Improvement Project. The SR-91 team used the presented tool to choose one of the two alternatives (maintain HOV SR-91 lane and add I-15 HOV lane using long-life Portland Cement Concrete Pavement or add Express Lane to SR-91 and I-15 using long-life Continuously Reinforced Concrete Pavement and Asphalt Concrete Pavement), equating to an estimated life-cost savings of $32 M.

An innovative Primary Surface Profile-based three-dimensional pavement distress data filtering approach for optical instruments and tilted pavement model-related noise reduction

The automatic pavement management system has the advantage of providing reliable pavement maintenance and rehabilitation strategies aiming at prolonging existing pavement service life. Therefore, the quality of noise reduction results, which is an unavoidable process of automatic pavement assessment evaluation, has a significant influence on the reliability of pavement maintenance operations suggested. The primary purpose of this paper is to propose an innovative three-dimensional (3D) pavement image-based data filtering protocol, thereby maintaining a highly functional pavement surface. First, a 3D pavement depth data collection system was developed using laser light and a charge-coupled device camera. After that, based on the analysis of Positive Noise and Negative Noise, which are optical instrument-related noises, and tilted pavement model noise, the Primary Surface Profile (PSP)-based raw data filtering approach was proposed which aims at improving the noise reduction quality. Validation experiments were conducted using both the proposed approach and the traditional data filtering method, and the results show that for the not tilted pavement surface model, the PSP-based filter method can achieve the highest noise reduction value (NRV), whereas for the tilted pavement surface model, with a slightly lower NRV than that of biphasic standard deviation average filtering, which demonstrates that the proposed data filtering method has self-adaptive and robust data filter advantages which can be incorporated into a high-performance pavement performance evaluation and management system.

An adaptive hybrid genetic algorithm for pavement management

The pavement maintenance and rehabilitation (M&R) strategy selection problem is an exceedingly hard problem to solve optimally. In this paper, a novel Adaptive Hybrid Genetic Algorithm (AHGA) is proposed which incorporates Local Search (LS) techniques into Genetic Algorithms (GA) to improve the overall efficiency and effectiveness of the search. Specifically, it contains two dynamic learning mechanisms to guide and combine the exploration and exploitation search processes adaptively. The first learning mechanism aims to assess the worthiness of conducting an LS reactively and to control the computational resources allocated to the application of this search technique efficiently. The second learning mechanism uses instantaneously learned probabilities to select from a set of pre-defined LS operators which compete against each other for selection which is the most appropriate at any particular stage of the search to take over from the evolutionary-based search process. The new AHGA is compared to a non-hybridized version of the GA by applying the algorithms to several case studies in order to determine the best pavement M&R strategy that minimizes the present value of the total M&R costs. The results show that the proposed AHGA statistically outperforms the traditional GA in terms of efficiency.

Prediction of performance and evaluation of flexible pavement rehabilitation strategies

Abstract Five test sections with different additives and strategies were established to rehabilitate a State-maintained highway more effectively in Rhode Island (RI): control, calcium chloride, asphalt emulsion, Portland cement and geogrid. Resilient moduli of subgrade soils and subbase materials before and after full depth rehabilitation were employed as input parameters to predict the performance of pavement structures using AASHTOWare Pavement ME Design (Pavement ME) software in terms of rutting, cracking and roughness. It was attempted to use Level 1 input (which includes traffic full spectrum data, climate data and structural layer properties) for Pavement ME. Traffic data was obtained from a Weigh-in-Motion (WIM) instrument and Providence station was used for collecting climatic data. Volumetric properties, dynamic modulus and creep compliance were used as input parameters for 19 mm (0.75 in.) warm mix asphalt (WMA) base and 12.5 mm (0.5 in.) WMA surface layer. The results indicated that all test sections observed AC top-down (longitudinal) cracking except Portland cement section which passed for all criteria. The order in terms of performance (best to worst) for all test sections by Pavement ME was Portland cement, calcium chloride, control, geogrid, and asphalt emulsion. It was also observed that all test sections passed for both bottom up and top down fatigue cracking by increasing thickness of either of the two top asphalt layers. Test sections with five different base/subbase materials were evaluated in last two years through visual condition survey and measurements of deflection and roughness to confirm the prediction, but there was no serious distress and roughness. Thus these experiments allowed selecting the best rehabilitation/reconstruction techniques for the particular and/or similar highway, and a framework was formulated to select an optimal technique and/or strategy for future rehabilitation/reconstruction projects. Finally, guidelines for long-term evaluation were developed to verify short-term prediction and performance.