A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications

Structural damage identification based on autoencoder neural networks and deep learning

An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm

Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.

Applications of artificial intelligence for disaster management

The use of intelligent computational tools for damage detection and identification with an emphasis on composites – A review

The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection

Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using Artificial Neural Network

Non-probabilistic wavelet method to consider uncertainties in structural damage detection

This paper presents the experimental results on the strength of shear key connection in precast concrete construction. The use of shear key is to connect two separate precast components to increase the shear resistivity of the joint surfaces. The proposed shear key shape in this study comprises of triangular, composite rectangular, semi-circle and trapezoidal. In addition, the trapezoidal shape is made up with 3 different key’s angles. All specimens are tested using the “push-off” method to obtain the ultimate shear capacity of which is due to the failure of the connection. From the analysis, stiffness, elastic and plastic behaviour, and the mode of failure is discussed to determine the most effective shape of the proposed shear key. From the findings, semi-circle shear key produced the highest shear capacity at 62.9 kN compared to that of the other shapes. Meanwhile, the trapezoidal shape at an angle of 45 produced the highest shear capacity at 44.1 kN. Together in the aspect of stiffness, the 45 trapezoidal shapes produced the highest resistance towards slip at 166.7 kN/mm. Failure mode are mostly due to shear, sliding and diagonal tension crack. Large slip of 7.35 mm is recorded from the triangular shape. The large slip is maybe due to sliding since the angle of the key faces to the shear plane is 45° which indicates to less interlocking compared with the other shapes they form an angle of 90° with the shear plane.

Ultimate shear capacity and failure of shear key connection in precast concrete construction

Optimum mix design of high-performance concrete containing micro POFA using historical data design

Khairul H. Padil

Papers

The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection

Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using Artificial Neural Network

Non-probabilistic wavelet method to consider uncertainties in structural damage detection

Ultimate shear capacity and failure of shear key connection in precast concrete construction

Optimum mix design of high-performance concrete containing micro POFA using historical data design