In an era of massive construction, damaged and aging infrastructure are becoming more common. Defects, such as cracking, spalling, etc., are main types of structural damage that widely occur. Hence, ensuring the safe operation of existing infrastructure through health monitoring has emerged as an important challenge facing engineers. In recent years, intelligent approaches, such as data-driven machines and deep learning crack detection have gradually dominated over traditional methods. Among them, the semantic segmentation using deep learning models is a process of the characterization of accurate locations and portraits of cracks using pixel-level classification. Most available studies rely on single-model knowledge to perform this task. However, it is well-known that the single model might suffer from low variance and low ability to generalize in case of data alteration. By leveraging the ensemble deep learning philosophy, a novel collaborative semantic segmentation of concrete cracks method called Co-CrackSegment is proposed. Firstly, five models, namely the U-net, SegNet, DeepCrack19, DeepLabV3-ResNet50, and DeepLabV3-ResNet101 are trained to serve as core models for the ensemble model Co-CrackSegment. To build the ensemble model Co-CrackSegment, a new iterative approach based on the best evaluation metrics, namely the Dice score, IoU, pixel accuracy, precision, and recall metrics is developed. Results show that the Co-CrackSegment exhibits a prominent performance compared with core models and weighted average ensemble by means of the considered best statistical metrics.

Real-time monitoring of cable tension is essential in detecting damage and assessing the service performance of bridges. Vibration frequency-based methods for cable tension identification have been increasingly investigated in bridge engineering over the last decade. However, cable tension estimation using vibration frequency-based methods can only obtain the average cable tension over a specified time interval. Therefore, identification of instantaneous variation of cable tension still remains to be a pending issue. To tackle this problem, this study proposes an algorithm defined as Synchrosqueezing Wave-packet-based Instantaneous Frequency Tracking (SWIFT) to identify the instantaneous change of cable tension by monitoring its acceleration responses. The proposed algorithm can extract the time-varying instantaneous frequency of the tested cable and further identify the variation of instantaneous cable tension by time-frequency analysis of the transversal motion. Moreover, the identification accuracy and noise robustness are numerically verified using a synthetic time-varying multi-frequencies signal. Finally, experimental validation is conducted on a prototype cable and an actual cable of the Sutong Yangtze River bridge, respectively. The theoretical, numerical, and experimental results show that the proposed method can precisely identify instantaneous cable tension under noisy conditions.

Cables have been increasingly utilized in modern long-span or tied-arch bridges as the main bearing structures. Real-time identification of time-varying cable tension is essential for assessing the service performance of bridges. Vibration-based methods have been an increasing research focus in recent decades. However, a long time interval is needed to estimate structural frequency using vibration-based methods, increasing the calculating time of cable tension. The time-varying cable tension is thus difficult to extract. This study proposes a time-frequency reassignment-based algorithm to reduce the detection time to address this issue. Combined with a time-frequency analysis tool and vibration theory of cables, the algorithm can identify the time-varying frequency and further quickly calculate the time-varying cable tension within 12.8 s. The features of the proposed algorithm are mainly threefold: identifying the time-varying frequencies with high precision; without some prior knowledge of vibration; having no other requirements for sensor modes. Moreover, the experimental validation is conducted using a quasi-static loading in a workshop and a dynamic field test on Sutong Bridge, respectively. The results show that the proposed algorithm can be used to identify time-varying tension and assess the service performance of cables, providing a new path for real-time condition monitoring of bridges in service.