Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.

The analysis of the optimal scalar and vector intensity measurements for seismic performance assessment of deep-buried hydraulic arched tunnels

The selection of optimal intensity measures(IMs)has been recommended for generating the seismic demand models with different probabilities by researchers since the seismic IMs are closely associated with earthquake risks and structural safety.However,the seismic design code(mainly for aboveground structures)and dynamic analysis of underground structures conventionally employ the peak ground acceleration(PGA)as an optimal IM.In this paper,the research is to identify the optimal scalar and vector IMs in the fragility investigation of deep-buried hydraulic arched tunnels using the finite element method.A refinement process was performed to determine the optimal scalar IMs by comprehensively comparing their correlation,efficiency,practicality,proficiency,and sufficiency among the examined IMs.Furtherly,the optimum vector IMs were also developed,followed by the three different scalar IMs.Eventually,the dif-ferences between the fragility curves of the tunnel produced using the optimal scalar and vector IM were compared.The generated vector fragility surface can be used to estimate the seismic fragility of identical hydraulic tunnels in an approximative manner.

A mortar contact formulation using scaled boundary isogeometric analysis

Contact analysis is recognized as being the most challenging problem in computational mechanics,because the functional system of contact problems is nonlinear and non-smooth and the convergence and accuracy of contact algorithms are difficult to guarantee.In the traditional finite element method(FEM)-based contact analysis[1,2],the contact body is spatially discretized,and the contact boundary is described using a low-order Lagrange interpolation polynomial.