Pavement Condition Index (PCI) is a numerical assessment of pavement conditions based on existing distresses. The PCI values are used for pavement management and rehabilitation programs. Calculating the PCIs using conventional method relies on collecting relevant field data (such as distresses types and severity) by visual inspection method. The collected data are processed to estimate the PCI values, which is a lengthy process that requires technical experience. This research aims to model the relationship between distresses type and severity and PCIs via straightforward and adaptive model. Therefore, Artificial Neural Networks (ANN) capabilities are employed to predict the PCI values of the different sections, thus reducing the required efforts and technical experiences to estimate PCI values. Moreover, the use of ANN enables the possibility of introducing new localized variables, such as the presence of manholes in pavement sections. The total of 348 directional sections from 10 different roads located in the City of Nablus, Palestine were examined to collect the distresses-related data and to estimate the corresponding PCI values using ASTM 6433‑07 method. The results revealed low correlation between distresses and PCI, where the highest absolute correlation between PCI and any distress type and severity did not exceed 0.38. The results indicated that the ANN model is capable to predicting the PCI with high level of reliability, with an R² value of 0.9971, 0.9964 and 0.9975 for training, validation and testing datasets, respectively. The regression slope between observed and predicted PCIs ranges between 0.9964 and 0.9974.

路面状况指数（PCI）是基于现有病害的路面状况的数值评估。PCI值用于路面管理和修复程序。使用常规方法计算PCI依赖于通过目视检查方法收集相关的现场数据（例如遇险类型和严重性）。处理收集到的数据以估计PCI值，这是一个漫长的过程，需要技术经验。这项研究旨在通过直接和自适应的模型来模拟遇险类型和严重程度与PCI之间的关系。因此，采用了人工神经网络（ANN）功能来预测不同部分的PCI值，从而减少了估算PCI值所需的工作量和技术经验。而且，使用ANN可以引入新的局部变量，例如人行道区域中是否存在人孔。检查了来自巴勒斯坦纳布卢斯市10条不同道路的348个定向路段，以收集与遇险有关的数据并使用ASTM 6433-07方法估算相应的PCI值。结果显示，遇险与PCI之间的相关性较低，其中PCI与任何遇险类型和严重性之间的最高绝对相关性不超过0.38。结果表明，ANN模型能够以较高的可靠性预测PCI，且具有R 检查了巴勒斯坦以收集与遇险有关的数据，并使用ASTM 6433-07方法评估了相应的PCI值。结果显示，遇险与PCI之间的相关性较低，其中PCI与任何遇险类型和严重性之间的最高绝对相关性不超过0.38。结果表明，ANN模型能够以较高的可靠性预测PCI，且具有R 检查了巴勒斯坦以收集与遇险有关的数据，并使用ASTM 6433-07方法评估了相应的PCI值。结果显示，遇险与PCI之间的相关性较低，其中PCI与任何遇险类型和严重性之间的最高绝对相关性不超过0.38。结果表明，ANN模型能够以较高的可靠性预测PCI，且具有R训练，验证和测试数据集的²值分别为0.9971、0.9964和0.9975。观察到的PCIs与预测的PCIs之间的回归斜率在0.9964至0.9974之间。