Al-Khateeb Ghazi G, Alnaqbi Ali, Zeiada Waleed
Department of Civil and Environmental Engineering, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates.
Department of Civil Engineering, Jordan University of Science and Technology, Irbid, 22110, Jordan.
Sci Rep. 2024 Sep 12;14(1):21301. doi: 10.1038/s41598-024-69999-9.
Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling-a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP's functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling.
连续配筋混凝土路面（CRCP）因其连续的钢筋配筋对交通基础设施的韧性至关重要，但它面临着一个关键挑战，即剥落——一种对路面耐久性和整体结构完整性构成威胁的病害现象。混凝土从表面脱离或破碎会损害CRCP的功能，并引发安全问题，增加维护成本。为解决这一紧迫问题，我们的研究调查了影响剥落的多方面因素，采用了一种综合方法，将统计和机器学习技术集成用于预测建模。描述性统计详细剖析了数据集，重点关注路面年龄、厚度、降水量、温度和交通参数。回归分析揭示了关键关系，强调了路面年龄、年温度、年降水量、最大湿度和初始国际平整度指数（IRI）作为影响因素的重要性。相关矩阵热图指导特征选择，阐明复杂的相互依存关系。同时，特征重要性分析确定路面年龄、年平均日交通量（AADT）和路面总厚度是剥落的关键促成因素。在机器学习中，采用包括高斯过程回归和集成树模型在内的模型，是基于它们的多种能力以及对当前复杂任务的适用性。它们不同的预测准确性凸显了明智选择模型的重要性。本研究通过对影响CRCP剥落的因素提供细致入微的见解，推进了路面工程实践，深化了我们对剥落影响的理解。因此，该研究不仅为开发改进的预测方法开辟了道路，还提高了CRCP基础设施的耐久性，对交通基础设施管理中的明智决策具有更广泛的意义。选择高斯过程回归和集成树模型源于它们能够适应捕捉数据集中的复杂关系，它们的比较性能为这些模型在CRCP剥落背景下的不同预测能力提供了有价值的见解。
