Laboratory study on the performance of recycled concrete aggregates blended with reclaimed asphalt pavement as pavement granular material

Production of recycled concrete aggregates (RCA) from construction and demolition (C&D) waste has become popular all over the world since the availability of land spaces are limited to dispose. Therefore it is important to seek alternative applications for RCA. The use of RCA in base and sub-base layers in granular pavement is a viable solution. In mechanistic pavement design, rutting (permanent deformation) is considered as the major failure mechanisms of the pavement. The rutting is the accumulation of permanent deformation of pavement layers caused by the repetitive vehicle load. In Queensland, Australia, it is accepted to have the maximum of 20% of reclaimed asphalt pavement (RAP) in RCA and therefore, it is important to investigate the effect of RAP on the permanent deformation properties of RCA. In this study, a series of repeated load triaxial (RLT) tests were conducted on RCA blended with different percentage of RAP to investigate the permanent deformation and resilient modulus properties of RCA. The vertical deformation and resilient modulus values were used to determine the response of RCA for the cyclic loading under standard pressure and loading conditions.

Falling Weight Deflectometer (FWD) tests on granular pavement reinforced with geogrids– Case study

The use of geogrids in granular pavement layers could increase the modulus and the stiffness of granular layer and hence the required layer thickness can be reduced. Though, geogrids are being used in granular pavements to provide lateral restraint, bearing capacity, and membrane tension support, very limited studies have been carried out to investigate the effects of geogrids on modulus and stiffness of granular layer. In this study, two sections of a granular pavement were constructed: one with a geogrid at the bottom of the base layer and the other without a geogrid. Two sections were then tested using Falling Weight Deflectometer (FWD) and FWD results were analysed to determine the effect of geogrid on the overall modulus and stiffness of the granular pavement. The results suggested that the pavement section with geogrid has higher overall modulus and deflection ratio compared to the pavement section without geogrid.

Influence of Spatial Variability on Pavement Responses Using Latin Hypercube Sampling on Two-Dimensional Random Fields

Although uncertainties in material properties have been addressed in the design of flexible pavements, most current modeling techniques assume that pavement layers are homogeneous. The paper addresses the influence of the spatial variability of the resilient moduli of pavement layers by evaluating the effect of the variance and correlation length on the pavement responses to loading. The integration of the spatially varying log-normal random field with the finite-difference method has been achieved through an exponential autocorrelation function. The variation in the correlation length was found to have a marginal effect on the mean values of the critical strains and a noticeable effect on the standard deviation which decreases with decreases in correlation length. This reduction in the variance arises because of the spatial averaging phenomenon over the softer and stiffer zones generated because of spatial variability. The increase in the mean value of critical strains with decreasing correlation length, although minor, illustrates that pavement performance is adversely affected by the presence of spatially varying layers. The study also confirmed that the higher the variability in the pavement layer moduli, introduced through a higher value of coefficient of variation (COV), the higher the variability in the pavement response. The study concludes that ignoring spatial variability by modeling the pavement layers as homogeneous that have very short correlation lengths can result in the underestimation of the critical strains and thus an inaccurate assessment of the pavement performance. (C) 2014 American Society of Civil Engineers.

Long Term Pavement Performance computed parameter : frost penetration /

"HRDI-13/11-08(1M)E"--P. 4 of cover.

A study on the performance of flexible pavements on mature soil subgrades

The country has witnessed tremendous increase in the vehicle population and increased axle loading pattern during the last decade, leaving its road network overstressed and leading to premature failure. The type of deterioration present in the pavement should be considered for determining whether it has a functional or structural deficiency, so that appropriate overlay type and design can be developed. Structural failure arises from the conditions that adversely affect the load carrying capability of the pavement structure. Inadequate thickness, cracking, distortion and disintegration cause structural deficiency. Functional deficiency arises when the pavement does not provide a smooth riding surface and comfort to the user. This can be due to poor surface friction and texture, hydro planning and splash from wheel path, rutting and excess surface distortion such as potholes, corrugation, faulting, blow up, settlement, heaves etc. Functional condition determines the level of service provided by the facility to its users at a particular time and also the Vehicle Operating Costs (VOC), thus influencing the national economy. Prediction of the pavement deterioration is helpful to assess the remaining effective service life (RSL) of the pavement structure on the basis of reduction in performance levels, and apply various alternative designs and rehabilitation strategies with a long range funding requirement for pavement preservation. In addition, they can predict the impact of treatment on the condition of the sections. The infrastructure prediction models can thus be classified into four groups, namely primary response models, structural performance models, functional performance models and damage models. The factors affecting the deterioration of the roads are very complex in nature and vary from place to place. Hence there is need to have a thorough study of the deterioration mechanism under varied climatic zones and soil conditions before arriving at a definite strategy of road improvement. Realizing the need for a detailed study involving all types of roads in the state with varying traffic and soil conditions, the present study has been attempted. This study attempts to identify the parameters that affect the performance of roads and to develop performance models suitable to Kerala conditions. A critical review of the various factors that contribute to the pavement performance has been presented based on the data collected from selected road stretches and also from five corporations of Kerala. These roads represent the urban conditions as well as National Highways, State Highways and Major District Roads in the sub urban and rural conditions. This research work is a pursuit towards a study of the road condition of Kerala with respect to varying soil, traffic and climatic conditions, periodic performance evaluation of selected roads of representative types and development of distress prediction models for roads of Kerala. In order to achieve this aim, the study is focused into 2 parts. The first part deals with the study of the pavement condition and subgrade soil properties of urban roads distributed in 5 Corporations of Kerala; namely Thiruvananthapuram, Kollam, Kochi, Thrissur and Kozhikode. From selected 44 roads, 68 homogeneous sections were studied. The data collected on the functional and structural condition of the surface include pavement distress in terms of cracks, potholes, rutting, raveling and pothole patching. The structural strength of the pavement was measured as rebound deflection using Benkelman Beam deflection studies. In order to collect the details of the pavement layers and find out the subgrade soil properties, trial pits were dug and the in-situ field density was found using the Sand Replacement Method. Laboratory investigations were carried out to find out the subgrade soil properties, soil classification, Atterberg limits, Optimum Moisture Content, Field Moisture Content and 4 days soaked CBR. The relative compaction in the field was also determined. The traffic details were also collected by conducting traffic volume count survey and axle load survey. From the data thus collected, the strength of the pavement was calculated which is a function of the layer coefficient and thickness and is represented as Structural Number (SN). This was further related to the CBR value of the soil and the Modified Structural Number (MSN) was found out. The condition of the pavement was represented in terms of the Pavement Condition Index (PCI) which is a function of the distress of the surface at the time of the investigation and calculated in the present study using deduct value method developed by U S Army Corps of Engineers. The influence of subgrade soil type and pavement condition on the relationship between MSN and rebound deflection was studied using appropriate plots for predominant types of soil and for classified value of Pavement Condition Index. The relationship will be helpful for practicing engineers to design the overlay thickness required for the pavement, without conducting the BBD test. Regression analysis using SPSS was done with various trials to find out the best fit relationship between the rebound deflection and CBR, and other soil properties for Gravel, Sand, Silt & Clay fractions. The second part of the study deals with periodic performance evaluation of selected road stretches representing National Highway (NH), State Highway (SH) and Major District Road (MDR), located in different geographical conditions and with varying traffic. 8 road sections divided into 15 homogeneous sections were selected for the study and 6 sets of continuous periodic data were collected. The periodic data collected include the functional and structural condition in terms of distress (pothole, pothole patch, cracks, rutting and raveling), skid resistance using a portable skid resistance pendulum, surface unevenness using Bump Integrator, texture depth using sand patch method and rebound deflection using Benkelman Beam. Baseline data of the study stretches were collected as one time data. Pavement history was obtained as secondary data. Pavement drainage characteristics were collected in terms of camber or cross slope using camber board (slope meter) for the carriage way and shoulders, availability of longitudinal side drain, presence of valley, terrain condition, soil moisture content, water table data, High Flood Level, rainfall data, land use and cross slope of the adjoining land. These data were used for finding out the drainage condition of the study stretches. Traffic studies were conducted, including classified volume count and axle load studies. From the field data thus collected, the progression of each parameter was plotted for all the study roads; and validated for their accuracy. Structural Number (SN) and Modified Structural Number (MSN) were calculated for the study stretches. Progression of the deflection, distress, unevenness, skid resistance and macro texture of the study roads were evaluated. Since the deterioration of the pavement is a complex phenomena contributed by all the above factors, pavement deterioration models were developed as non linear regression models, using SPSS with the periodic data collected for all the above road stretches. General models were developed for cracking progression, raveling progression, pothole progression and roughness progression using SPSS. A model for construction quality was also developed. Calibration of HDM–4 pavement deterioration models for local conditions was done using the data for Cracking, Raveling, Pothole and Roughness. Validation was done using the data collected in 2013. The application of HDM-4 to compare different maintenance and rehabilitation options were studied considering the deterioration parameters like cracking, pothole and raveling. The alternatives considered for analysis were base alternative with crack sealing and patching, overlay with 40 mm BC using ordinary bitumen, overlay with 40 mm BC using Natural Rubber Modified Bitumen and an overlay of Ultra Thin White Topping. Economic analysis of these options was done considering the Life Cycle Cost (LCC). The average speed that can be obtained by applying these options were also compared. The results were in favour of Ultra Thin White Topping over flexible pavements. Hence, Design Charts were also plotted for estimation of maximum wheel load stresses for different slab thickness under different soil conditions. The design charts showed the maximum stress for a particular slab thickness and different soil conditions incorporating different k values. These charts can be handy for a design engineer. Fuzzy rule based models developed for site specific conditions were compared with regression models developed using SPSS. The Riding Comfort Index (RCI) was calculated and correlated with unevenness to develop a relationship. Relationships were developed between Skid Number and Macro Texture of the pavement. The effort made through this research work will be helpful to highway engineers in understanding the behaviour of flexible pavements in Kerala conditions and for arriving at suitable maintenance and rehabilitation strategies. Key Words: Flexible Pavements – Performance Evaluation – Urban Roads – NH – SH and other roads – Performance Models – Deflection – Riding Comfort Index – Skid Resistance – Texture Depth – Unevenness – Ultra Thin White Topping

Comportamento mecânico de um agregado reciclado a partir de resíduos de construção e demolição submetido a carregamentos cíclicos

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Programa de Pós-Graduação em Geotecnia, 2016.

Parametric study on cement-based soft-hard-soft (SHS) multi-layer composite pavement against blast load

An innovative cement-based soft-hard-soft (SHS) multi-layer composite has been developed for protective infrastructures. Such composite consists of three layers including asphalt concrete (AC), high strength concrete (HSC), and engineered cementitious composites (ECC). A three dimensional benchmark numerical model for this SHS composite as pavement under blast load was established using LSDYNA and validated by field blast test. Parametric studies were carried out to investigate the influence of a few key parameters including thickness and strength of HSC and ECC layers, interface properties, soil conditions on the blast resistance of the composite. The outcomes of this study also enabled the establishment of a damage pattern chart for protective pavement design and rapid repair after blast load. Efficient methods to further improve the blast resistance of the SHS multi-layer pavement system were also recommended.

Methodology for Pavement Design Reliability and Back Analysis Using Markov Chain Monte Carlo Simulation

Given the increasing cost of designing and building new highway pavements, reliability analysis has become vital to ensure that a given pavement performs as expected in the field. Recognizing the importance of failure analysis to safety, reliability, performance, and economy, back analysis has been employed in various engineering applications to evaluate the inherent uncertainties of the design and analysis. The probabilistic back analysis method formulated on Bayes' theorem and solved using the Markov chain Monte Carlo simulation method with a Metropolis-Hastings algorithm has proved to be highly efficient to address this issue. It is also quite flexible and is applicable to any type of prior information. In this paper, this method has been used to back-analyze the parameters that influence the pavement life and to consider the uncertainty of the mechanistic-empirical pavement design model. The load-induced pavement structural responses (e.g., stresses, strains, and deflections) used to predict the pavement life are estimated using the response surface methodology model developed based on the results of linear elastic analysis. The failure criteria adopted for the analysis were based on the factor of safety (FOS), and the study was carried out for different sample sizes and jumping distributions to estimate the most robust posterior statistics. From the posterior statistics of the case considered, it was observed that after approximately 150 million standard axle load repetitions, the mean values of the pavement properties decrease as expected, with a significant decrease in the values of the elastic moduli of the expected layers. An analysis of the posterior statistics indicated that the parameters that contribute significantly to the pavement failure were the moduli of the base and surface layer, which is consistent with the findings from other studies. After the back analysis, the base modulus parameters show a significant decrease of 15.8% and the surface layer modulus a decrease of 3.12% in the mean value. The usefulness of the back analysis methodology is further highlighted by estimating the design parameters for specified values of the factor of safety. The analysis revealed that for the pavement section considered, a reliability of 89% and 94% can be achieved by adopting FOS values of 1.5 and 2, respectively. The methodology proposed can therefore be effectively used to identify the parameters that are critical to pavement failure in the design of pavements for specified levels of reliability. DOI: 10.1061/(ASCE)TE.1943-5436.0000455. (C) 2013 American Society of Civil Engineers.

Large-Scale Controlled-Condition Experiment to Evaluate Light Weight Deflectometers for Modulus Determination and Compaction Quality Assurance of Unbound Pavement Materials

Compaction control using lightweight deflectometers (LWD) is currently being evaluated in several states and countries and fully implemented for pavement construction quality assurance (QA) by a few. Broader implementation has been hampered by the lack of a widely recognized standard for interpreting the load and deflection data obtained during construction QA testing. More specifically, reliable and practical procedures are required for relating these measurements to the fundamental material property—modulus—used in pavement design. This study presents a unique set of data and analyses for three different LWDs on a large-scale controlled-condition experiment. Three 4.5x4.5 m2 test pits were designed and constructed at target moisture and density conditions simulating acceptable and unacceptable construction quality. LWD testing was performed on the constructed layers along with static plate loading testing, conventional nuclear gauge moisture-density testing, and non-nuclear gravimetric and volumetric water content measurements. Additional material was collected for routine and exploratory tests in the laboratory. These included grain size distributions, soil classification, moisture-density relations, resilient modulus testing at optimum and field conditions, and an advanced experiment of LWD testing on top of the Proctor compaction mold. This unique large-scale controlled-condition experiment provides an excellent high quality resource of data that can be used by future researchers to find a rigorous, theoretically sound, and straightforward technique for standardizing LWD determination of modulus and construction QA for unbound pavement materials.