Reinforced concrete (RC) buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolutional neural networks have been adopted in recent research to rapidly quantify the damage state (DS) of structures. In this article, an advanced object detection neural network, named YOLOv2, is implemented, which achieves 98.2% and 84.5% average precision in training and testing, respectively. The proposed YOLOv2 is used in combination with the classification neural network, which improves the identification accuracy for critical DS of RC structures by 7.5%. The improved classification procedures allow engineers to rapidly and more accurately quantify the DSs of the structure, and also localize the critical damage features. The identified DS can then be integrated with the state‐of‐the‐art performance evaluation framework to quantify the financial losses of critical RC buildings. The results can be used by the building owners and decision makers to make informed risk management decisions immediately after the strong earthquake shaking. Hence, resources can be allocated rapidly to improve the resiliency of the community.

In the present work, we systematically study the α decay half-lives of neptunium ( Z = 93) isotopes within a one-parameter model (OPM). The calculated results are in reasonable agreement with the experimental data. Noticeably, the recently measured α decay half-life value of 380 − 110 + 260 ns for ²²² Np [ Phys. Rev. Lett. 125 , 032 502 (2020)] has been fairly reproduced by the OPM as 780 ns within a factor of ≈2. In addition, we extend this model to predict α decay half-lives of neptunium isotopes whose α decay is energetically allowed or observed but not yet quantified in NUBASE2016. Meanwhile, it is found that the half-life between the neutron number N = 126 and N = 128 in Np drop sharply, this result shows that the N = 126 shell closure is still very robust at Z = 93. At last, α particle preformation probability P α extd are extracted in two cases with α transition orbital angular momentum l = 0 and l = 1, we find a amazing phenomenon that the P α extd are basically the same for α transition with the same orbital angular momentum l .

Computer vision techniques have gained great traction in civil infrastructure inspection and monitoring. This paper conducted a systematic review of recent data-driven computer vision algorithms in structural damage detection published during the past 5 years. The theories of prevalent computer vision models are first reviewed with an emphasis on the progressive innovation in algorithms’ architecture. Then, recent applications of computer vision models for structural damage evaluation are discussed, which are classified into different structural categories by their material types (i.e., concrete, steel, masonry, timber) at three hierarchical levels including damage recognition, localization, and quantification. In particular, the paper also highlights the current state of using computer vision for damage assessment of timber structures, which remains under-explored compared to concrete and steel structures. Next, the paper scrutinizes existing structural damage inspection guidelines to identify key technological gaps between the capability of existing computer vision methods and manual inspection practices in the field. Finally, the paper summarizes existing challenges and recommends future research opportunities including the integration of computer vision methods with multimodal large language models, sensor-fusion, and mobile inspection approaches.