Optimum use of the flexible pavement condition indicators in pavement management system
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This study aimed at investigating the current practices and methods adopted by roads agencies around the world with regard to collection, analysis and utilization of the data elements pertaining to the main pavement condition indicators in pavement management systems (PMS). It also aimed at identifying the main predictors associated with each condition indicator and the factors that govern pavement structural and functional performance. Development of a new performance index that incorporates parameters or measures related to the main condition indicators (surface defects, roughness, deflection and skid resistance) and establishing the weight to be assigned to each indicator based on the relative impact on pavement condition was also one of the main objectives of this study. Thousands of pavement sections were subjected to thorough testing and inspection over the last few years to collect data pertaining to the main condition indicators. The collected data encompass visual distress survey, deflection measurements, roughness and skid resistance measurements. Collection of various condition indicators was accomplished according to well known international standards. The collected data were processed, tabulated and analyzed for the purpose of development of performance models and to prove certain theories or good practices.Advanced tools and machines were utilized to collect these data with a high degree of accuracy. The Falling Weight Deflectometer (FWD) was used to collect deflection data for structural analysis. Two Non-contact laser roughness measuring devices mounted on vehicles were heavily used for collecting roughness, texture, and rutting data. Distress data were collected using a manual procedure adopted and standardized at the Pavement Management System Unit of Dubai Emirate. Powerful engineering and statistical softwares were used in the analysis for the purpose of processing the data, back calculating the main parameters pertaining to pavement response, establishing the correlation matrices between various dependent variables and their predictors, and finally, applying linear and non linear regression analysis to develop reliable and predictable deterioration models for the uses of pavement management system. The analysis procedure was supplemented by a vast literature review for the up to date information along within deep investigations and verifications for some of the current practices, theories and models used in pavement design and pavement evaluation with more emphasis on the inherent drawbacks associated to these models and procedures. The study confirmed that pavement condition deterioration and performance can be best predicted and evaluated based on four main condition indicators; First, surface distress to assess the physical condition of the pavements and detect the inherent problems and defects caused by various factors affecting pavement performance. Second; roughness measurements to evaluate the riding quality of the pavement.Third; deflection to calculate pavement response (stress and strains) and to assess pavement structural capacity and calculating the remaining life, and finally, skid resistance measurement to assess the level of safety and surface texture properties. Thorough study and investigation of the physical condition indicators and the associated parameters, confirmed that pavement distress data are vital elements in each pavement management system. Distress data can be used effectively to identify the main problems associated with pavement performance, causes of deterioration, maintenance measures needed to prevent the acceleration of the distress, the rehabilitation schemes needed to improve the pavement condition and finally to prepare maintenance work programs and to estimate the annual maintenance needs under an open or limited budget. Alligator cracking was found to have the heaviest impact on pavement condition. Distress density, probable causes of deterioration and distress propagation rate are the required parameters in PMS. Roughness was found to have a basic influence on pavement condition and the type of selected treatment. The use of Roughness data in terms of International Roughness Index (IRI) can be optimized in PMS by using this indicator in the following forms:Roughness, as an objective measure, can be used as a good performance predictor of the current riding quality of pavements in service and reflects the inherent imperfections and built-in irregularities embodied in the road pavement surface. Roughness measurement can be used as a reference to establish construction specifications and provides through the PMS system an organized feedback approach to correct the persistent design deficiencies detected after road construction. Roughness can be used effectively in the planning process for maintenance works and to select the candidate sections through calculating the functional remaining life based on the estimated terminal value using Roughness-Age, Roughness-ESAL, and Roughness-PSI models. Lane–IRI along with the Difference between the left and right wheel IRI values, termed as “ Yaw” are the most suitable forms to be used in PMS to report about roughness characteristics. Yaw term can be used effectively to report or feed back about geometric imperfections that exist on the road surface such as improper cross slope, shoving and the probable drainage problems. The roughness cumulative distribution curves can be used as a planning tool in PMS to report at the network level. These curves indicate the network health and the required funding at different level of risks, so proactive measures can be taken and the required budgets can be made available.Deflection data were found to form a basic component of the PMS. It was found that these data can be used at both network and project levels. Direct deflection measurements were found Not to be the ideal form to report about structural capacity at the network level. It is rather can be used at project level to detect weak spots and critical pavements layers. At the network level, the back calculated parameters from deflection basin such as Pavement Modulus (Ep), Asphalt and Pavement Curvature (CUR), Cross Sectional Area and the other deflection basin characteristics are much more appropriate for reporting about pavement structural conditions and calculating the structural remaining life in PMS. The design deflection and curvature that characterize the pavement have been found to be calculated based on the mean along with the two times the standard deviation of the measured data. The Effective Structural Number (SNeff) was found to have good correlations with the Total Pavement Thickness (Ht), the value of the deflection measured at the center of the loading plate ( D0 ) and the difference between D0 and the deflection measured at 450mm from the center of the loading plate ( D0 - D450 ). The first two variables were found to account for more than 92% of the structural capacity prediction model.Traffic variable in terms of the accumulated standard repetitions (ESAL) was found to account for more than 60% of the deflection model predictability. Other variables such as E value, asphalt and base layer thicknesses can improve the predictability of the model if included. The concept of the relative value of effective pavement modulus to the original pavement modulus (Eeff/E0) was found to gives a reliable representation about the exhausted and the remaining life of the in-service pavement structure. The study showed that the pavement is reported to be structurally failed, when the effective asphalt or pavement modulus is about 20 - 35 % of its original design value which is equal to the modulus of the unbound material. It was also found that when the area of the fatigue cracking and the patching distresses exceeds 17% of the total pavement section area, or the depth of rutting is more than 15mm, the pavement is reported to be structurally failed and major rehabilitation or reconstruction should be applied. Skid resistance can be reported in the form of International Friction Index (IFI), as a well defined universal index, along with other two numbers; F60 Friction (Microtexture) related number measured at 60 km/h velocity and Macrotexture related number and Vp, which constitute the IFI index can be used in Pavement management system applications to report about skid resistance characteristics and the network level of safety. These three figures can be used to report about pavement condition, accidents, airports operations, and maintenance management surveys.In this study, new methods and models were developed and suggested to be used in PMS as an alternative to the current available methods which were found to be impractical in certain cases. Finally, further research efforts are recommended to explore the uses of other parameters in particular those related to deflection basin analysis, cross sectional area, curvature, and pavement moduli. Skid resistance testing and reporting method should be subjected to further research works for the purpose of standardizing reporting methods, identifying the relative impact of main predictors i.e. megatexture, macrotexture and microtexture components and to develop performance models.
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