CALCULATION AND ANALYSIS OF THE PHYSICAL STOREY DRIFT FOR HIGH-RISE FRAME-DIAGRID STRUCTURES

The high-rise frame-diagrid structure is a new type of dual system structure. The inner frame part can create a large space, and the external diagrid part can provide a larger lateral stiffness. In this paper, the lateral deformation formula for the high-rise frame-diagrid structures is derived. The bending deformation of the structure is divided into the bending rotational deformation and the floor rigid rotational deformation. The physical storey drift is proposed. The physical storey drift is directly related to the structural damage. When the structure is in the plastic state, the structure maximum storey drift and maximum physical storey drift are in different positions. It is recommended to use both storey drift and physical storey drift as structural deformation limitation criteria. Finally, the proposed method is used to structural parameters analysis for the high-rise frame-diagrid structure. It provides reference for the structural design

Stationary Solution of Duffing Oscillator Driven by Additive and Multiplicative Colored Noise Excitations

A bistable Duffing oscillator subjected to additive and multiplicative Ornstein-Uhlenbeck (OU) colored excitations is examined. It is modeled through a set of four first-order stochastic differential equations by representing the OU excitations as filtered Gaussian white noise excitations. Enlargement in the state-space vector leads to four-dimensional (4D) Fokker-Planck-Kolmogorov (FPK) equation. The exponential-polynomial closure (EPC) method, proposed previously for the case of white noise excitations, is further improved and developed to solve colored noise case, resulting in much more polynomial terms included in the approximate solution. Numerical results show that approximate solutions from the EPC method compare well with the predictions obtained via Monte Carlo simulation (MCS) method. Investigation is also carried out to examine the influence of intensity level on the probability distribution solutions of system responses

Study on Mechanical Properties and Mesoscopic Numerical Simulation of Recycled Concrete

To obtain the mechanical properties of recycled concrete (RC) under different replacement rates (RRs) of recycled coarse aggregate (RCA), a quasi-static uniaxial compression test, a uniaxial splitting tension test and a uniaxial dynamic compression test of RC with replacement rates (RRs) of 0%, 20%, 40%, 60%, 80% and 100% were carried out. ABAQUS was used to investigate the cracking and failure of RC. The results showed that, when the RR was less than 60%, the uniaxial compressive and tensile strengths of RC decreased with the increase in RR, but slightly increased when RR was greater than 80%. Under impact load, the dynamic compressive strength of RC increased linearly with the increase in the strain rate, showing an obvious strain rate effect. With the increase in RR, the strain rate sensitivity of RC gradually decreased. The concrete damage plastic (CDP) model can describe the mechanical behavior of RC well. The damage and failure of RC occurred first at the old interface transition zone (ITZ) and old mortar. As the strain rate increases, the damage and failure rate of the specimens is intensified. Research on the mechanical properties of RC can provide a basis for the application and promotion of RC technology

Study on Mechanical Properties and Mesoscopic Numerical Simulation of Recycled Concrete

To obtain the mechanical properties of recycled concrete (RC) under different replacement rates (RRs) of recycled coarse aggregate (RCA), a quasi-static uniaxial compression test, a uniaxial splitting tension test and a uniaxial dynamic compression test of RC with replacement rates (RRs) of 0%, 20%, 40%, 60%, 80% and 100% were carried out. ABAQUS was used to investigate the cracking and failure of RC. The results showed that, when the RR was less than 60%, the uniaxial compressive and tensile strengths of RC decreased with the increase in RR, but slightly increased when RR was greater than 80%. Under impact load, the dynamic compressive strength of RC increased linearly with the increase in the strain rate, showing an obvious strain rate effect. With the increase in RR, the strain rate sensitivity of RC gradually decreased. The concrete damage plastic (CDP) model can describe the mechanical behavior of RC well. The damage and failure of RC occurred first at the old interface transition zone (ITZ) and old mortar. As the strain rate increases, the damage and failure rate of the specimens is intensified. Research on the mechanical properties of RC can provide a basis for the application and promotion of RC technology

Axial Cyclic Testing of Concrete-Filled Steel Tube Columns in Diagrid Structures

Inclined concrete-filled steel tube (CFST) columns in a diagrid structure system can efficiently carry large vertical loads and horizontal forces. This paper presents an experimental study of the stress characteristics of engineered inclined CFST columns under axial cyclic loading. Ten specimens were tested, including two hollow steel tube (HST) columns and eight CFST columns, and the influences of loading scheme, aspect ratio, concrete strength, and steel ratio were examined. The seismic behaviours were investigated, including mechanical behaviour, failure modes and hysteretic curves, and ductility, and the interaction between the steel tube and concrete was examined as well. Better ductility and energy dissipation capacity are achieved in the tension direction, whereas higher bearing capacity and stiffness are achieved in the compression direction. Compared with hollow steel tube columns, the supporting effect of concrete on the steel tube for CFST columns in tension and the restraining effect of the steel tube on concrete for CFST columns in compression ensure higher capacity, deformability, and energy dissipation capacity

Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation

Melanoma is a lethal skin cancer. In its diagnosis, skin lesion segmentation plays a critical role. However, skin lesions exhibit a wide range of sizes, shapes, colors, and edges. This makes skin lesion segmentation a challenging task. In this paper, we propose an encoding–decoding network called Res-CDD-Net to address the aforementioned aspects related to skin lesion segmentation. First, we adopt ResNeXt50 pre-trained on the ImageNet dataset as the encoding path. This pre-trained ResNeXt50 can provide rich image features to the whole network to achieve higher segmentation accuracy. Second, a channel and spatial attention block (CSAB), which integrates both channel and spatial attention, and a multi-scale capture block (MSCB) are introduced between the encoding and decoding paths. The CSAB can highlight the lesion area and inhibit irrelevant objects. MSCB can extract multi-scale information to learn lesion areas of different sizes. Third, we upgrade the decoding path. Every 3 × 3 square convolution kernel in the decoding path is replaced by a diverse branch block (DBB), which not only promotes the feature restoration capability, but also improves the performance and robustness of the network. We evaluate the proposed network on three public skin lesion datasets, namely ISIC-2017, ISIC-2016, and PH2. The dice coefficient is 6.90% higher than that of U-Net, whereas the Jaccard index is 10.84% higher than that of U-Net (assessed on the ISIC-2017 dataset). The results show that Res-CDD-Net achieves outstanding performance, higher than the performance of most state-of-the-art networks. Last but not least, the training of the network is fast, and good results can be achieved in early stages of training

A Lightweight Context-Aware Feature Transformer Network for Human Pose Estimation

We propose a Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design the Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes 3×3 convolution in the bottleneck of HRNet with a Contextual Transformer (CoT) block while reducing the complexity of the network. Specifically, the CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on query and contextualization keys, dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module (AFAM). Technically, given an intermediate input, the AFAM successively deduces attention maps along the channel and spatial dimensions. Then, an adaptive refinement module (ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above procedures, our lightweight network provides powerful clues for the detection of keypoints. Experiments are performed on the COCO and MPII datasets. The model achieves a 76.2 AP on the COCO val2017 dataset. Compared to other methods with a CNN as the backbone, CaFTNet has a 72.9% reduced number of parameters. On the MPII dataset, our method uses only 60.7% of the number of parameters, acquiring similar results to other methods with a CNN as the backbone

Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation

Melanoma is a lethal skin cancer. In its diagnosis, skin lesion segmentation plays a critical role. However, skin lesions exhibit a wide range of sizes, shapes, colors, and edges. This makes skin lesion segmentation a challenging task. In this paper, we propose an encoding–decoding network called Res-CDD-Net to address the aforementioned aspects related to skin lesion segmentation. First, we adopt ResNeXt50 pre-trained on the ImageNet dataset as the encoding path. This pre-trained ResNeXt50 can provide rich image features to the whole network to achieve higher segmentation accuracy. Second, a channel and spatial attention block (CSAB), which integrates both channel and spatial attention, and a multi-scale capture block (MSCB) are introduced between the encoding and decoding paths. The CSAB can highlight the lesion area and inhibit irrelevant objects. MSCB can extract multi-scale information to learn lesion areas of different sizes. Third, we upgrade the decoding path. Every 3 × 3 square convolution kernel in the decoding path is replaced by a diverse branch block (DBB), which not only promotes the feature restoration capability, but also improves the performance and robustness of the network. We evaluate the proposed network on three public skin lesion datasets, namely ISIC-2017, ISIC-2016, and PH2. The dice coefficient is 6.90% higher than that of U-Net, whereas the Jaccard index is 10.84% higher than that of U-Net (assessed on the ISIC-2017 dataset). The results show that Res-CDD-Net achieves outstanding performance, higher than the performance of most state-of-the-art networks. Last but not least, the training of the network is fast, and good results can be achieved in early stages of training

The Simple Mix Design Method and Confined Behavior Analysis for Recycled Aggregate Concrete

For the environment protection and sustainable development in building construction, waste concrete can be processed into recycled aggregate to mix the recycled aggregate concrete (RAC). However, the existing mix design methods of RAC were complex, and the mechanical properties of RAC were more weakened than ordinary concrete. This paper presents a simple mix design method for RAC, including orthogonal test and single-factor test. Then, in order to study the behavior of confined RAC, this paper presents a comprehensive experimental study on the RAC filled in steel tube (RCFST) specimens and the RAC filled in GFRP tube (RCFST) specimens. The results show that the proposed mix design method can mix different stable strength grades of RAC promptly and efficiently. In addition, the steel tube and GFRP tube can provide a well confining effect on core RAC to improve the mechanical behavior of column. Moreover, the properties of core RAC in steel tube are the same as the common passive confined concrete, and the properties of core RAC in the GFRP tube are the same as the common active confined concrete. The study results can provide reference for other kinds of RAC mixtures as well as be a foundation for theoretical studies on confined RAC