Zhang Tonghao, Mahdi Mohammad, Issa Mohsen, Xu Chenxi, Ozevin Didem
Department of Civil, Materials, and Environmental Engineering, University of Illinois Chicago, 929 West Taylor Street, Chicago, IL 60607, USA.
Sensors (Basel). 2023 Oct 10;23(20):8356. doi: 10.3390/s23208356.
Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width.
玄武岩纤维增强聚合物（BFRP）增强混凝土因其高抗拉强度和耐腐蚀特性，是传统钢筋混凝土的一种新型替代材料。然而，由于BFRP是一种脆性材料，用BFRP增强的混凝土结构可能会发生意外破坏。在本研究中，采用声发射（AE）方法作为结构健康监测（SHM）解决方案，对BFRP增强混凝土板的损伤起始和发展进行监测。除了高分辨率相机、应变和位移传感器外，还在两块简支板上布置了一系列AE传感器，然后加载直至破坏。主要的破坏机制是由于超筋设计和BFRP筋与混凝土之间的良好粘结导致混凝土开裂。对AE方法在识别损伤起始、从拉伸裂缝到剪切裂缝的发展以及裂缝宽度演变方面进行了评估。将无监督机器学习应用于从第一次板试验获得的AE数据，以形成损伤机制的聚类。使用应用于从第二块板获得的数据的k均值监督学习模型对聚类结果进行验证。在预测三个聚类（拉伸裂缝、剪切裂缝和噪声）时，在第一块板上训练的K-NN模型的准确率为99.2%。由于单一指标表征复杂损伤特性的局限性，进行了统计夏普利加法解释（SHAP）分析，以量化每个AE特征对裂缝宽度的贡献。基于SHAP分析，AE持续时间与裂缝宽度的相关性最高。裂缝附近AE传感器的累积持续时间跟踪裂缝宽度的准确率接近100%。得出的结论是，位于板跨中的AE传感器可作为一种有效的SHM解决方案，用于监测拉伸裂缝的起始、重大损伤导致的结构响应突然变化、从拉伸裂缝到剪切裂缝的损伤演变以及裂缝宽度的发展。
