Applications of non-destructive testing devices such as Falling Weight Deflectometer (FWD) provide crucial estimates of pavement health that assist in the optimisation of pavement management systems. However, regularly conducting these tests at a network level and post-processing of the collected data is cumbersome, which requires technical expertise, significant time, funds, and other resources. Due to this structural aspect of pavements during the selection of maintenance or repair, decisions are often ignored. This study attempts to develop reliable correlations for estimates of two different deflection basin parameters using a number of structural, functional, environmental, and subgrade soil attributes as input. The data has been obtained through field tests over a 124 km long pavement network. Different artificial neural network-based models are trained by varying the number of hidden layers and neurons in these layers, for the above-mentioned purpose. The coefficient of determination and mean square error is decisive for the selection of best network architecture. These outcomes are also compared to the results of the classical multiple linear regression method, and the superiority of neural networks over non-intelligent approaches for non-linear problems of pavement engineering is appreciated. In addition to this, the results justify the fact that the properties of the asphalt layer predominantly impact the entire pavement condition. The proposed approach is an alternative way to facilitate quick pavement condition assessment by reducing the frequency of deflection testing without compromising with the accuracy of its estimates. It would encourage the increased application of structural condition data in pavement maintenance and rehabilitation necessities with ease. However, the study does not intend to completely avoid conducting deflection testing and serve as a base for future studies.

落锤式挠度计 (FWD) 等无损检测设备的应用提供了对路面健康状况的重要估计，有助于优化路面管理系统。然而，在网络级别定期进行这些测试并对收集的数据进行后处理很麻烦，这需要技术专长、大量时间、资金和其他资源。由于在选择维护或维修期间路面的这种结构方面，决策经常被忽略。本研究尝试使用许多结构、功能、环境和路基土壤属性作为输入，为两个不同的偏转盆地参数的估计建立可靠的相关性。这些数据是通过 124 公里长的路面网络的现场测​​试获得的。不同的基于人工神经网络的模型通过改变隐藏层和这些层中神经元的数量来训练，用于上述目的。决定系数和均方误差对于选择最佳网络架构是决定性的。还将这些结果与经典多元线性回归方法的结果进行了比较，并赞赏神经网络在解决路面工程非线性问题的非智能方法上的优越性。除此之外，结果证明了这样一个事实，即沥青层的特性主要影响整个路面状况。所提出的方法是通过减少挠度测试的频率而不影响其估计的准确性来促进快速路面状况评估的替代方法。它将鼓励结构条件数据在路面维护和修复方面的应用越来越容易。然而，该研究并不打算完全避免进行挠度测试并作为未来研究的基础。