Structural damage detection is still a challenging problem owing to the difficulty of extracting damage-sensitive and noise-robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low-level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise-free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.

Structural damage detection (SDD) still suffers from environmental uncertainties or modeling errors, causing a gap between the numerical model and the real structure. It results in performance degradation in the application of many model‐based methods, which are usually designed on a numerical model and needed to be applied to a real structure. Such a situation is defined as a cross‐domain SDD problem in this work. This paper aims to address the cross‐domain SDD problem by designing a feature‐extractor to generate both damage‐sensitive and domain‐invariant features, instead of trying to reduce the gap, as the traditional methods do. A domain adaptation (DA) neural network is designed and trained on the data from both the numerical model and the real structure at the same time. In addition, no damage label of the real structure is needed. Both numerical and laboratory experiments show that the proposed method has excellent performance and outperforms the baseline model, a traditional convolutional neural network (CNN). This paper provides a new methodology to solve the cross‐domain SDD problem, that is, to learn better features instead of just trying to reduce the gap.

Dynamic compressive and tensile properties of mortar under impact loading were investigated experimentally by using a split Hopkinson pressure bar (SHPB) apparatus with pulse shaping technique. Firstly, the basic principle, experimental limitations and some feasible improvements/modifications of SHPB technique used for dynamic tests on concrete-like materials were summarized briefly. And then the dynamic compressive strength, stress versus strain response, and failure modes of mortar were discussed and analyzed. Finally, a dynamic Brazilian disc test was conducted to obtain the splitting tensile property of mortar, and some typical experimental results were presented. Both compressive and splitting tensile results show that mortar is a strain-rate sensitive material. Either compressive or tensile strength enhances with the increase of strain rate, especially when the strain rate is greater than the transition strain rate, which is around 20 s^-1 for the dynamic compression and 2.0 s^-1 for the splitting tension, respectively. These findings are helpful to guide the design and application of concrete structures.