Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Abstract

Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro‐fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. These parameters are generally obtained by performing falling weight deflectometer tests and monitoring the development of distresses on the pavement surface. The proposed hybrid model for predicting pavement performance was characterized by multilayer, feedforward neural networks that led the reasoning process of the IF‐THEN fuzzy rules. The results of the neuro‐fuzzy model were superior to those of the linear regression model in terms of accuracy in the approximation. The proposed neuro‐fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques.

RETRACTED: Stiffness performance of polyethylene terephthalate modified asphalt mixtures estimation using support vector machine-firefly algorithm

“…The theory of resilient modulus master curve could be used effectively for modelling the resilient modulus of asphalt concrete mixtures at different loading frequencies and elevated temperatures [37]. Alessandra Bianchini and Paola Bandini [38] evaluated a neuro-fuzzy model to estimate the performance of flexible pavement. They used parameters that were mostly obtained from falling weight deflectometer tests and were generally provided by agencies to evaluate the pavement condition.…”

Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods

“…As a key component of a PMS, pavement performance prediction models play a critical role in providing highway agencies with decision support for their overall maintenance and budget plan (Yang et al 2002). The development of forecasting models that are capable of describing and predicting pavement performance accurately is critical for these agencies (Bianchini and Bandini 2010), and accurate predictions of pavement performance based on the periodic observations are vital for determining desirable maintenance actions and budget allocations (Pan et al 2011). Improved accuracy of pavement performance models can make a significant difference in the expenditure on pavement maintenance and rehabilitation (Yang et al 2002).…”

“…Pavement management systems (PMSs) apply analytical tools and statistical methods to assist highway agencies in decision-making procedures to maintain pavements in serviceable and functional conditions throughout their life (Bianchini and Bandini 2010). As a key component of a PMS, pavement performance prediction models play a critical role in providing highway agencies with decision support for their overall maintenance and budget plan (Yang et al 2002).…”

Impact of Allocation the Resources on Decision-Making in Course of Planning Processes through Design Predictive Model Using Fuzzy Logic Approach

“…Aggregation occurs only once for each output variable. The input to the aggregation process is truncated output fuzzy sets returned by the implication process for each rule [18]. The out-put of the aggregation process is the combined output fuzzy set.…”