Transportation agencies should measure pavement performance to appropriately strategize road preservation, maintenance, and rehabilitation activities. The international roughness index (IRI), which is a means to quantify pavement roughness, is a primary performance indicator. Many attempts have been made to correlate pavement roughness to other pavement performance parameters. Most existing correlations, however, are based on traditional statistical regression, which requires a hypothesis for the data. In this study, a novel approach was developed to predict asphalt concrete (AC) pavement IRI, utilizing datasets extracted from the Long-Term Pavement Performance (LTPP) database. IRI prediction is categorized by two models: (i) IRI progression over the pavement’s service life without maintenance/rehabilitation and (ii) the drop in IRI after maintenance. The first model utilizes the recurrent neural network algorithm, which deals with time-series data. Therefore, historical traffic data, environmental information, and distress (rutting, fatigue cracking, and transverse cracking) measurements were extracted from the LTPP database. A long short-term memory network was used to solve the vanishing gradient problem. Finally, an optimal model was achieved by setting the sequence length to 2 years. The second model utilizes an artificial neural network algorithm to correlate the impacting factors to the IRI value after maintenance. The impacting factors include maintenance activities; initial (new construction), milled, and overlaid AC thicknesses; as well as IRI value before maintenance activities. Combining the two models allows for the prediction of IRI values over AC pavement’s service life.

运输机构应衡量路面性能，以制定适当的道路保护、维护和修复活动策略。国际粗糙度指数（IRI）是量化路面粗糙度的一种手段，是主要的性能指标。已经进行了许多尝试以将路面粗糙度与其他路面性能参数相关联。然而，大多数现有的相关性都基于传统的统计回归，这需要对数据进行假设。在这项研究中，开发了一种利用从长期路面性能 (LTPP) 数据库中提取的数据集来预测沥青混凝土 (AC) 路面 IRI 的新方法。IRI 预测分为两种模型：(i) 在没有维护/修复的情况下，在路面的使用寿命期间 IRI 的进展和 (ii) 维护后 IRI 的下降。第一个模型利用循环神经网络算法，处理时间序列数据。因此，从 LTPP 数据库中提取了历史交通数据、环境信息和故障（车辙、疲劳裂纹和横向裂纹）测量值。使用长短期记忆网络解决梯度消失问题。最后，通过将序列长度设置为 2 年来实现最佳模型。第二种模型利用人工神经网络算法将影响因素与维护后的 IRI 值相关联。影响因素包括维护活动；初始（新结构）、铣削和叠加 AC 厚度；以及维护活动前的 IRI 值。结合这两个模型可以预测 AC 路面使用寿命内的 IRI 值。