Philip Babitha, AlJassmi Hamad
Department of Civil and Environmental Engineering, UAE University, Al Ain, P.O.Box 15551, United Arab Emirates.
Emirates Center for Mobility Research (ECMR), UAE University, Al Ain, P.O.Box 15551, United Arab Emirates.
Heliyon. 2024 Feb 6;10(3):e25625. doi: 10.1016/j.heliyon.2024.e25625. eCollection 2024 Feb 15.
Over time, the pavement deteriorates due to traffic and the environment, resulting in poor riding quality and structural inadequacies. Evaluating pavement condition over time is thus a critical component of any pavement management system (PMS) to extend the service life of pavements. However, the uncertainty associated with the pavement deterioration process due to the heterogeneous nature of the pavement degradation factors makes the process difficult. The current work addresses this challenge of pavement management by developing an expert system framework based on Bayesian Belief Networks (BBN). This framework integrates data on existing road deterioration factors with knowledge gained from pavement experts to produce optimal decisions. The advantages of the BBN techniques lie in their ability to capture uncertainty, and probabilistically infer the values of variables in the domain, especially in the case of incomplete information where we only have data about some and not all variables. This has motivated the adoption of BBN in this study to optimize pavement maintenance decisions, on the basis of inferred road deterioration interpretations drawn from partial knowledge about road distress variables. This study presents the adoption of Bayesian methods to assist pavement maintenance engineers in determining the most successful and efficient maintenance and repair (M&R) tactics and the best time to apply them by means of a decision-support system. Data collected from 32 road sections in the United Arab Emirates in relation to road distress parameters (rutting, deflection, cracking, and international roughness index), as well as road characteristics, traffic, and environment data, has been used to demonstrate the applicability of the proposed decision-support tool.
随着时间的推移，路面会因交通和环境因素而恶化，导致骑行质量下降和结构缺陷。因此，评估路面随时间的状况是任何路面管理系统（PMS）延长路面使用寿命的关键组成部分。然而，由于路面退化因素的异质性，与路面恶化过程相关的不确定性使得这一过程变得困难。当前的工作通过开发基于贝叶斯信念网络（BBN）的专家系统框架来应对路面管理的这一挑战。该框架将现有道路退化因素的数据与从路面专家那里获得的知识相结合，以做出最优决策。BBN技术的优势在于其能够捕捉不确定性，并以概率方式推断领域中变量的值，特别是在信息不完整的情况下，即我们只有部分而非所有变量的数据。这促使本研究采用BBN来优化路面维护决策，基于从道路病害变量的部分知识得出的推断道路退化解释。本研究提出采用贝叶斯方法，通过决策支持系统协助路面维护工程师确定最成功、最有效的维护和修复（M&R）策略以及应用这些策略的最佳时机。从阿拉伯联合酋长国32个路段收集的与道路病害参数（车辙、弯沉、裂缝和国际平整度指数）以及道路特征、交通和环境数据相关的数据，已被用于证明所提出的决策支持工具的适用性。
