Currently, Palestinian municipalities define pavement conditions using detailed visual inspection. This procedure is expensive and time consuming. The new proposed model contributes to reducing both time and cost incurred by identifying a new model that considers machine-learning techniques rather than the conventional ASTM procedure. The authors studied the most common six pavement defects. These defects are alligator cracks, patching, longitudinal, and transverse cracks, shoving, and potholes. An optimized hybrid model is developed to determine the Pavement Condition Index (PCI) based on the FHWA Long-Term Pavement Performance (LTPP) database. The model follows a cascade architecture with three classical machine learning models followed by a neural network model. Using the neural network model as a second stage in the chain allows the model to focus its power on the nonlinear curve estimation to further reduce the errors. Finally, out-of-sample performance analysis as well as a cross-validation analysis is carried out to check the robustness of the model. The results show that the model estimates PCI with high degree of accuracy.

目前，巴勒斯坦市政当局使用详细的目视检查来确定路面条件。该过程昂贵且耗时。新提议的模型通过确定考虑机器学习技术而非传统 ASTM 程序的新模型，有助于减少时间和成本。作者研究了最常见的六种路面缺陷。这些缺陷是鳄鱼裂纹、修补、纵向和横向裂纹、推挤和坑洼。基于 FHWA 长期路面性能 (LTPP) 数据库，开发了一种优化的混合模型来确定路面状况指数 (PCI)。该模型遵循级联架构，其中包含三个经典机器学习模型，然后是一个神经网络模型。使用神经网络模型作为链中的第二阶段，允许模型将其力量集中在非线性曲线估计上，以进一步减少误差。最后，进行样本外性能分析以及交叉验证分析以检查模型的稳健性。结果表明，该模型对PCI的估计精度较高。