The delayed brittle fracture of high-strength bolts in long-span steel bridges threatens the safety of the bridges and even lead to serious accidents. Currently, human periodic inspection, the most commonly applied detection method for this kind of high-strength bolts damage, is a dangerous process and consumes plenty of manpower and time. To detect the damage fast and automatically, a visual inspection approach based on deep learning is proposed. YOLOv3, an object detection algorithm based on convolution neural network (CNN), is introduced due to its good performance for the detection of small objects. First, a dataset including 500 images labeled for damage is developed. Then, the YOLOv3 neural network model is trained by using the dataset, and the capability of the trained model is verified by using 2 new damage images. The feasibility of the proposed detection method has been demonstrated by the experimental results.
Long time lasted micro vibration will easily cause the looseness of bolts. It is critical for the health and safety monitoring of bolt looseness. Although many remarkable bolt looseness detection approaches already been developed in recent years, it is still difficult to the quantitatively evaluate the bolt degradation without do any harm to the bolt. In this article, a FBG (Fiber Bragg Grating) embedded smart washer was proposed to monitor the looseness of bolt connection and the theory analysis were induced to verify the linear tendency of smart washer annular strain. Two experiments were carried out to verify the analysis: first of all, the relationship between applied torque and bolt tension were investigated. A smart bolt which made by embedding FBG sensor into the bolt was used in the specimen and the experiment result indicates that bolt axial tension is linearly with applied torques which also consistent with previous researches. Then, the experiment which fabricated with smart washer and smart bolt was implement. The linear relationship between pre-load change and the wavelength increment was obtained. The analytical and experimental results both demonstrate that the proposed novel approach in this study is very sensitive to the bolt pre-load degradation and it is robust in monitoring the blot looseness.
The problem of optimizing an absorber system for three-dimensional seismic structures is addressed. The objective is to
determine the number and position of absorbers to minimize the coupling effects of translation-torsion of structures at
minimum cost. A procedure for a multi-objective optimization problem is developed by integrating a dominance-based
selection operator and a dominance-based penalty function method. Based on the two-branch tournament genetic
algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. The
technique guarantees the better performing individual winning its competition, provides a slight selection pressure
toward individuals and maintains diversity in the population. Moreover, due to the evaluation for individuals in each
generation being finished in one run, less computational effort is taken. Penalty function methods are generally used to
transform a constrained optimization problem into an unconstrained one. The dominance-based penalty function contains
necessary information on non-dominated character and infeasible position of an individual, essential for success in
seeking a Pareto optimal set. The proposed approach is used to obtain a set of non-dominated designs for a six-storey
three-dimensional building with shape memory alloy dampers subjected to earthquake.
This paper focus on the seismic response control of eccentric structures using tuned liquid column dampers (TLCD) and
circular tuned liquid column dampers (CTLCD). An 8-story eccentric steel building, with two TLCDs on the orthogonal
direction and one CTLCD on the mass center of the top story, is analyzed. The optimal parameters of liquid dampers are
optimized by Genetic Algorithm. The structural response with and without liquid dampers under bi-directional
earthquakes are calculated. The results show that the torsionally coupled response of structures can be effectively
suppressed by use of liquid dampers with optimal parameters.
This paper presents studies of seismic response control of a frame structure braced with SMA (Shape Memory Alloy)
tendons through both numerical and experimental approaches. Based on the Brinson one-dimensional constitutive law
for SMAs, a two-story frame structure braced diagonally with SMA tendons is used as an example to simulate
numerically the vibration control process. By considering the temperature, different initial states and thermal properties
of the SMA tendon, and the variable intensity and frequency of earthquake input, the parameters of the system were
analyzed during the numerically simulation. The time histories of the displacement and hysteretic loops of the SMA
tendons were simulated under earthquake ground motion by using finite element method (FEM). To validate the
efficiency of the simulation, a shaking table test for the frame structure was conducted. Both numerical simulation and
experimental results show that the actively controlled martensite SMA tendons can effectively suppress the vibration of
the multi-story frame structure during an earthquake.
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