Concrete structural failures generally initiate with cracks. Effective control of cracks is the key to ensure the safety and durability of concrete structures. With the development of advanced sensing technology and bionic materials, more attention has been paid to the smart detection and healing for concrete cracks, which provides a new idea to improve the accuracy, efficiency and initiative of concrete crack control. This special issue focuses on the latest research theory and technology of smart detection and healing for concrete cracks.
With the development of self-healing technology, the overall properties of the microcapsule-enabled self-healing concrete have taken a giant leap. In this research, a detailed assessment of current research on the microcapsule-enabled self-healing concrete is conducted, together with bibliometric analysis. In the bibliometric analysis, various indicators are considered. The current state of progress regarding self-healing concrete is assessed, and an analysis of the temporal distribution of documents, organizations and countries of literature is conducted. Later, a discussion of the citations is analyzed. The research summarizes the improvements of microcapsule-enabled self-healing cementitious composites and provides a concise background overview.
An algorithm based on deep semantic segmentation called LC-DeepLab is proposed for detecting the trends and geometries of cracks on tunnel linings at the pixel level. The proposed method addresses the low accuracy of tunnel crack segmentation and the slow detection speed of conventional models in complex backgrounds. The novel algorithm is based on the DeepLabv3+ network framework. A lighter backbone network was used for feature extraction. Next, an efficient shallow feature fusion module that extracts crack features across pixels is designed to improve the edges of crack segmentation. Finally, an efficient attention module that significantly improves the anti-interference ability of the model in complex backgrounds is validated. Four classic semantic segmentation algorithms (fully convolutional network, pyramid scene parsing network, U-Net, and DeepLabv3+) are selected for comparative analysis to verify the effectiveness of the proposed algorithm. The experimental results show that LC-DeepLab can accurately segment and highlight cracks from tunnel linings in complex backgrounds, and the accuracy (mean intersection over union) is 78.26%. The LC-DeepLab can achieve a real-time segmentation of 416 × 416 × 3 defect images with 46.98 f/s and 21.85 Mb parameters.
Autogenous self-healing is the innate and fundamental repair capability of cement-based materials for healing cracks. Many researchers have investigated factors that influence autogenous healing. However, systematic research on the autogenous healing mechanism of cement-based materials is lacking. The healing process mainly involves a chemical process, including further hydration of unhydrated cement and carbonation of calcium oxide and calcium hydroxide. Hence, the autogenous healing process is influenced by the material constituents of the cement composite and the ambient environment. In this study, different factors influencing the healing process of cement-based materials were investigated. Scanning electron microscopy and optical microscopy were used to examine the autogenous healing mechanism, and the maximum healing capacity was assessed. Furthermore, detailed theoretical analysis and quantitative detection of autogenous healing were conducted. This study provides a valuable reference for developing an improved healing technique for cement-based composites.
