Mehmet Saltan

Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli

Through the new technological developments, for highway maintenance engineering the structural capacity of pavement is to be determined using non-destructive techniques. Up to now various methodologies have been applied based on the surface deflection bowl obtained under either a known moving wheel load or devices such as falling weight deflectometer. Backcalculating pavement layer moduli are well-accepted procedures in the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from non-destructive testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in situ material properties can be backcalculated by the measured field data for appropriate analysis techniques. To backcalculate reliable moduli, the deflection basin must be modeled more realistically. Here, in this study, the deflection basins measured on the surface of the flexible pavements are modeled using artificial neural networks (ANN) with cross-validation technique. Distances between transducers can be varied with different producer companies. The distances between transducer are used for the form deflection basin. Layer thickness and distance to loading center are used as input in the present study. Limited experimental deflection data groups from NDT are used to show the capability of the neural network technique in modeling the deflection bowl. Since enough data are not available to construct a reliable neural network, a methodology based on the cross-validation technique can be used. The results show that the proposed methodology give the deflection bowl satisfied accuracy.

Backcalculation of pavement layer moduli and Poisson's ratio using data mining

Pavement deflection data are often used to evaluate a pavements structural condition non-destructively. Pavement layers are characterized by their elastic moduli estimated from surface deflections through backcalculation. Using backcalculation analysis, flexible pavement layer in situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. This study focuses on the use of data mining (DM)-based pavement backcalculation tools for determining the in situ elastic moduli and Poissons ratio of asphalt pavement from synthetically derived Falling Weight Deflectometer (FWD) deflections at seven equidistant points. In estimation of the elastic modulus and Poissons ratio, data mining (DM) method has not been used as a backcalculation tool before. Experimental deflection data groups from NDT are used to show the capability of the DM approaches in backcalculating the pavement layer thickness and compared each other. By looking at the results of the study, Kstar method gives fine results with respect to other DM methods. Backcalculation of pavement layer elastic modulus and Poissons ratio with DM has been carried out for the first time.

Artificial neural networks–based backcalculation of the structural properties of a typical flexible pavement

Pavement evaluation is one of the foremost phases in all pavement engineering activities. In the backcalculation process, the researcher or the engineer varies the structural properties of the layers until the theoretical (calculated) deflections and the obtained (measured) deflections from FWD tests are closely matched to each other within a tolerable limit. However, this process is substantially time-consuming and poses some difficulties due to inherent inaccuracies in the results. In this study, synthetically derived deflections from a typical flexible pavement are used to estimate asphaltic concrete layer’s elastic modulus, Poisson’s ratio and thickness. Furthermore, artificial neural network (ANN) is utilized to determine the structural parameters, and it can be clearly seen that satisfactory results are obtained. ANN estimation of the three pavement layer characteristic parameters, that is, layer elastic modulus, Poisson’s ratio and layer thickness, was carried out for the first time in the study.

Utilization of Pumice Waste for Clayey Subgrade of Pavements

The present study examined the potential of pumice waste of the Isparta-Gelincik region, which has been categorized under the lightweight aggregate class as a stabilizing additive to problematic clayey subgrade of pavements. The physical properties of lightweight aggregate material were analyzed. In earlier research, few experiments have been carried out by using experimental samples to test for properties such as stability when frozen, solidity, strength, Atterberg limits, California bearing ratio (CBR), and dynamic repeated load triaxial (RLT) tests. The utilization of Gelincik pumice waste as a stabilizer was determined for the clayey subgrade. Pumice waste of the Isparta-Gelincik region and high plasticity clay were mixed in varying proportions to improve the engineering properties of the clayey soil. The CBR experiment was also performed to observe the change of strength through the use of waste material. In addition, RLT testing was performed by using mixed materials to observe the changes in the resilient moduli values of the mixtures. The results of the experimental research showed that Gelincik pumice waste can be used as a stabilizer for problematic clayey subgrades when constructing roads. This research is the first example of a stabilization approach for problematic clayey subgrade of pavements by using pumice waste.

Optimization of the deflection basin by genetic algorithm and neural network approach

This paper introduces a new concept of integrating artificial neural networks (ANN) and genetic algorithms (GA) in modeling the deflection basins measured on the flexible pavements. Backcalculating pavement layer moduli are well-accepted procedures for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from Nondestructive Testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in-situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. In order to backcalculate reliable moduli, deflection basin must be realistically modeled. In this work, ANN was used to model the deflection basin characteristics and GA as an optimization tool. Experimental deflection data groups from NDT are used to show the capability of the ANN and GA approach in modeling the deflection bowl. This approach can be easily and realistically performed to solve the optimization problems which do not have a formulation or function about the solution.

Use of glass waste as mineral filler in hot mix asphalt

Abstract The use of resources is increasing due to continuous increase in world population and rapid industrialization, while natural resources are being exhausted day by day. Usage of waste materials or by-products in highway construction has substantial environmental and economic benefits. In this study, the usage of cullet and waste glass bottle dust as mineral filler material in hot mix asphalt as an alternate to traditional crushed stone dust was investigated. Optimum bitumen content was determined by the Marshall mix design method by using six different bitumen contents (4.0%, 4.5%, 5.0%, 5.5%, 6.0%, and 6.5%). With the optimum bitumen content, three different mineral filler types (cullet, glass bottle waste, and stone dust) and six different filler ratios (4%, 5%, 6%, 7%, 8%, and 9%) were used to prepare asphalt mixture samples. Samples were performed using the Marshall stability test, and the results were compared. It is concluded that cullet and glass bottle waste can be used in asphalt mixtures as a mineral filler alternate to crushed stone dust if the economic and environmental factors favor it.

Backcalculation of pavement layer thickness using data mining

Pavement deflection data are often used to evaluate a pavement’s structural condition nondestructively. Pavement layers are important parameters in view of bearing capacity. Pavement layer thickness may be known from the design project or site investigation. At the same time, using backcalculation analysis, flexible pavement layer thicknesses together with in situ material properties can also be backcalculated from the measured field data through appropriate analysis techniques. Data mining (DM) process has not been used as a backcalculation tool before. In this study, DM process is used in backcalculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the DM process in backcalculating the pavement layer thickness and compared each other. Performing the study, modeling with Kstar method gives fine results with respect to other DM modeling techniques. Backcalculation of pavement layer thickness using DM process has been carried out for the first time.

Backcalculation of Pavement Layer Thickness and Moduli Using Adaptive Neuro-fuzzy Inference System

Efficient and economical methods are important in determination of the structural properties of the existing flexible pavements. An important pavement monitoring activity performed by most highway agencies is the collection and analysis of deflection data. Pavement deflection data are often used to evaluate a pavement’s structural condition non-destructively. It is essential not only to evaluate the structural integrity of an existing pavement but also to have accurate information on pavement structural condition in order to establish a reasonable pavement rehabilitation design system. Pavement structural adequacy is often evaluated by calculating elastic modulus of each layer using the so-called “backcalculation”. Backcalculating the pavement layer properties is a well-accepted procedure for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from Nondestructive Testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, flexible pavement layer thicknesses together with in-situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. In this study, adaptive neural based fuzzy inference system (ANFIS) is used in backcalculating the pavement layer thickness and moduli from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the ANFIS approaches in backcalculating the pavement layer thickness and moduli, and compared each other.

Prediction of the marshall stability of reinforced asphalt concrete with steel fiber using fuzzy logic

In this study, Marshall Stability MS of steel fiber reinforced asphalt concrete has been predicted using steel fiber rate 0%, 0.25%, 0.50%, 0.75%, 1.0%, 1.5%, 2.0% and 2.5%, bitumen content 5%, 5.5% and 6.0% and unit weights 2,465--2,515 gr/cm3 by Fuzzy Logic FL. Results have shown that developed FL model has a strong potential for predicting the MS of asphalt concrete without performing any experimental studies.

Feasibility of Detecting Debonding of Hot Mix Asphalt Layer with Nondestructive Testing

Sufficient bonding between the hot mix asphalt (HMA) layers is essential to ensure the desired structural capacity of a pavement. Undetected delamination can ultimately result in the peeling of thin overlays of asphalt concrete from the surface of the roadway. Further progression of delamination may result in stripping of the lower layers due to the intrusion of moisture. Rapid nondestructive test (NDT) methods determine the existence and extent of delamination or stripping within the asphalt pavements. In this paper, several NDT procedures and/or equipment that have the potential to address the problem were identified, and their effectiveness and potential for success were evaluated. The identified NDT methods, which included the Ground Penetrating Radar, Thermography, sonic/seismic and impulse response, were evaluated on a controlled pavement section that was specifically constructed with various levels of debonding at different depths and with different asphalt mixes. Strengths and limitations of different methods are discussed in this paper.