ABSTRACT

Though several attempts have been made in the past to develop performance prediction models for flexible road pavement in Nigeria, insufficient dataset remained the major problem of using such models. This study used Fuzzy logic theory which is capable of handling the challenge of imprecise dataset to develop a framework for performance prediction of flexible road pavement in Nigeria. A Fuzzy Inference System was developed using MATLAB software. The attributes used included; Initial Pavement Surface Condition, Age of pavement, Resilient Modulus of subgrade soil, Average Truck load per day, Average Annual Air Temperature and Rainfall to predict the Future Pavement Surface Condition (FPSC). The model was calibrated using observed logical behaviour of pavement to fit engineering experience and judgement. A goodness-of-fit test showed high level of accuracy at 81.63% which was validated at 74.13% using extrapolated dataset of the same source. The study proposed 5120 mutually exclusive decision rules for performance prediction of flexible road pavement based on permutation theory. Though there was no current and well-spread dataset that described the present pavement condition to calibrate the decision rules, a framework for performance prediction of flexible road pavement using Fuzzy logic theory was developed for pavement engineers in Nigeria.

摘要

尽管过去曾多次尝试开发尼日利亚柔性道路路面的性能预测模型，但数据集不足仍然是使用此类模型的主要问题。本研究使用能够处理不精确数据集挑战的模糊逻辑理论来开发尼日利亚柔性路面性能预测的框架。使用 MATLAB 软件开发了一个模糊推理系统。使用的属性包括；初始路面表面状况、路面年龄、路基土的弹性模量、每天平均卡车负载、平均年气温和降雨量，以预测未来路面表面状况 (FPSC)。该模型使用观察到的路面逻辑行为进行校准，以适应工程经验和判断。拟合优度测试显示准确率为 81.63%，使用相同来源的外推数据集验证为 74.13%。研究提出了5120条互斥决策规则，用于基于置换理论的柔性路面性能预测。尽管没有当前且广泛使用的数据集来描述当前路面状况以校准决策规则，但为尼日利亚的路面工程师开发了一个使用模糊逻辑理论预测柔性路面性能的框架。