Abstract
Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness Index (IRI) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Adaptive Neuro-Fuzzy Inference System (ANFIS). A historical database of 14 years was employed for predicting the IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The optimum ANFIS structure comprises a hybrid learning algorithm with six fuzzy rules of generalized bell curve membership functions (Gbellmf) for the DBST model and nine fuzzy rules of two-sided Gaussian membership functions (Gauss2mf) for the AC model. Both models used the constant membership function for the output variable (IRI). The statistical evaluation results revealed that both ANFIS models (DBST and AC) have a good prediction capacity with high values of coefficient of determination (R2 0.93 and 0.88) and low values of Mean Absolute Error (MAE 0.28 and 0.27) and Root Mean Squared Percentage Error (RMSPE 7.03 and 9.98). In addition, results revealed that ANFIS models yielded higher prediction accuracy than Multiple Linear Regression (MLR) models previously developed under the same conditions.
Highlights
Academic Editor: Qiao DongThe American Association of State Highway and Transportation Officials (AASHTO)defines a Pavement Management System (PMS) as “a set of tools or methods that assist decision-makers in finding optimum strategies for providing, evaluating, and maintaining pavements in a serviceable condition over a period of time” [1]
In Double Bituminous Surface Treatment (DBST) modeling, the optimum Adaptive Neuro-Fuzzy Inference System (ANFIS) structure consists of three generalized bell curve membership functions (Gbellmf) for AGE input and two Gbellmf for Cumulative Equivalent Single-Axle Load (CESAL) input, and six rules, and being trained for 335 epochs
For Asphalt Concrete (AC) modeling, the optimum ANFIS structure consists of three Gauss2mf for both AGE and YESAL, and nine rules, and being trained for 250 epochs to prevent overfitting
Summary
Defines a PMS as “a set of tools or methods that assist decision-makers in finding optimum strategies for providing, evaluating, and maintaining pavements in a serviceable condition over a period of time” [1]. If the HDM-4 models’ equations were used without calibration, they would predict pavement conditions that may not accurately match those observed on specific road sections [13,14]. For these reasons, calibration of the HDM-4 models to local conditions is both desirable and rational [12,15]. The calibration of the HDM-4 IRI models requires detailed and precise distress data, for instance: initial IRI (IRI0 ) value, environmental coefficient, adjusted structural number (ASN), cracking area (CR), rutting depth (RUT), and the number of potholes per km [15–17] Such data records are not fully available for Laos yet, making it difficult to calibrate the HDM-4 IRI prediction models for local conditions
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