Application of Adaptive Neuro–Fuzzy Inference System for Forecasting Pavement Roughness in Laos
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Asphalt Pavement Performance Evaluation · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-Machi, Nagasaki 852-8521, Japan
Faculty of Engineering, National University of Laos, Lao-Thai Road, Sokpaluang Village, Sisatanak District, Vientiane Capital 01030, Laos
Funding
Japan International Cooperation Agency
References (54)
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