A stochastic imperfection simulation method from high-fidelity measurements on cold-formed steel studs

This paper presents a stochastic imperfection simulation method towards cold-formed steel (CFS) studs, the data of which is generated from a high-thruput laser measurement device. An accurate hand-held laser scanner is applied to investigate about 19 types of CFS studs that widely used in current Chinese markets. point cloud models of which are reconstructed. A developed imperfection pattern recognition algorithm efficiently processes the high-fidelity point cloud models and automatically characterizes the imperfections based on the local, distortional, and global buckling modes. The measured imperfection data is statistically analyzed, where statistical models and inter-correlation models of mode imperfections are carefully obtained thereafter. The inter-correlation matrices and statistical models are parametrized for stochastic analysis. A data-driven stochastic imperfection simulation method thus is proposed based on the as-real correlation parameters and statistical data. The generated imperfection models are compared with the measured imperfections to validate the proposed simulation method. The investigation provides consolidated data foundations for the imperfection sensitivity analysis. The evaluation of CFS studs’ reliability can be insightfully conducted; thus, the imperfection design in the general specification is advised. The proposed imperfection simulation method based on inter-correlation parameters and as-measured statistical models also contributes to the development of trending simulation-based design of cold-formed steel structures.This work was financially supported by Quota Project for Promoting the Connotation Development of Colleges and Universities - Young Scholars of Beijing Talent Project (02082721010), Basic Research Funds for Municipal Universities (X21062). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author (s) and do not necessarily reflect the views of the sponsors or other participants

Poly(delta-gluconolactone) and Poly(delta-gluconolactone- ε

Poly(delta-gluconolactone) (PGL) and poly(delta-gluconolactone-ε-caprolactone) (P(GL-CL)) were synthesized through ring-opening polymerization (ROP) and characterized by FT-IR, NMR, XRD, intrinsic viscosity, GPC, DSC, and TGA. The crystallinity of P(GL-CL) with various d-GL/CL ratios (d-GL/CL = 5 : 5, 4 : 6, 3 : 7, 2 : 8, and 1 : 9) was 12.09 to 59.78% while PGL was amorphous. Melting temperature (Tm) of these polymers was 49.8 to 62.0°C and decomposition temperature was 282 to 489°C depending on the d-GL/CL ratios. In addition, all these polymers were degradable and the degradation rates could be controlled by adjusting d-GL/CL ratios. These results indicated that PGL and P(GL-CL) might be promising novel absorbable materials

A new data-enabled intelligence framework for evaluating urban space perception

The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space

A stochastic imperfection simulation method from high-fidelity measurements on cold-formed steel studs

This paper presents a stochastic imperfection simulation method towards cold-formed steel (CFS) studs, the data of which is generated from a high-thruput laser measurement device. An accurate hand-held laser scanner is applied to investigate about 19 types of CFS studs that widely used in current Chinese markets. point cloud models of which are reconstructed. A developed imperfection pattern recognition algorithm efficiently processes the high-fidelity point cloud models and automatically characterizes the imperfections based on the local, distortional, and global buckling modes. The measured imperfection data is statistically analyzed, where statistical models and inter-correlation models of mode imperfections are carefully obtained thereafter. The inter-correlation matrices and statistical models are parametrized for stochastic analysis. A data-driven stochastic imperfection simulation method thus is proposed based on the as-real correlation parameters and statistical data. The generated imperfection models are compared with the measured imperfections to validate the proposed simulation method. The investigation provides consolidated data foundations for the imperfection sensitivity analysis. The evaluation of CFS studs’ reliability can be insightfully conducted; thus, the imperfection design in the general specification is advised. The proposed imperfection simulation method based on inter-correlation parameters and as-measured statistical models also contributes to the development of trending simulation-based design of cold-formed steel structures.This work was financially supported by Quota Project for Promoting the Connotation Development of Colleges and Universities - Young Scholars of Beijing Talent Project (02082721010), Basic Research Funds for Municipal Universities (X21062). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author (s) and do not necessarily reflect the views of the sponsors or other participants

Evaluation of Compressive Geophysical Prospecting Method for the Identification of the Abandoned Goaf at the Tengzhou Section of China’s Mu Shi Expressway

Subsidence deformation of abandoned goafs can induce cracking, distortion and even collapse of surface buildings (structures), and thus, subsidence deformation poses a great threat. Accurate detection of the abandoned goaf location and overburden morphology is an important prerequisite for stability evaluation and scientific management of surface buildings (structures), and effective detection methods are bottlenecks for accurate detection. Taking the abandoned goaf in the Tengzhou section of China’s Mu Shi expressway as an engineering example, step-by-step detection, traditional detection and combination methods are used to determine the location of the underlying abandoned goaf and overburden morphology. First, we conduct disaster investigation on the expressway and surface within the affected area of the abandoned goaf and initially determine the detection area. Then, according to the principle that the detection range can be examined step-by-step from large to small, the high-density resistivity method is used for detection, and the high-resolution seismic method is further selected to analyze the target area. Then, based on the results of the resistivity method, the position of the abandoned goaf is evaluated with the high-resolution seismic method, and the distributions of the overburden subsidence, the water-filled fractured zone and the caving zone (the three belts) are determined. Finally, boreholes are drilled deep into the bottom of the abandoned goaf at specific locations and the distributions of the abandoned goaf and three belts are verified and corrected with drilling data, acoustic detection and borehole TV imaging technology, thereby providing accurate data on abandoned goafs for highway stability evaluation

Blast Resistance in Sandwich Structures Based on TPMS

This study analyzes the blast resistance in triple-period minimal surface (TPMS) sandwich panel structures with a cellular structure. The explosion test of the TPMS sandwich panel was carried out, and experimental data verified the effectiveness of the finite element model. Four TPMS configurations, Diamond, Gyroid, IWP, and Primitive, were selected as the core of the sandwich panel to determine the dynamic response process of the TPMS sandwich panel under the action of a blast load. The effects of the thickness of the core material and the explosive charge on the blast resistance in the TPMS sandwich panel were investigated. The results show that the increase in core thickness reduces the blast energy absorption efficiency of the sandwich panel, and the energy resistance in the Diamond configuration sandwich panel is stronger than the other three configurations under the same blast load; the increase in explosive charge significantly increases the displacement of the sandwich panel, and the Gyroid configuration shows better energy absorption effect; different TPMS configurations and panel thickness have a significant effect on the deformation and energy absorption of the sandwich panel under the blast load. The results of this study can promote the application of TPMS sandwich structures in blast-resistant structures

A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception

The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space

3-D Gabor-based anisotropic diffusion for speckle noise suppression in dynamic ultrasound images

Speckle noise contaminates medical ultrasound images, and the suppression of speckle noise is helpful for image interpretation. Traditional ultrasound denoising (i.e., despeckling) methods are developed on two-dimensional static images. However, one of the advantages of ultrasonography is its nature of dynamic imaging. A method for dynamic ultrasound despeckling is expected to incorporate both the spatial and temporal information in successive images of dynamic ultrasound and thus yield better denoising performance. Here we regard a dynamic ultrasound video as three-dimensional (3-D) images with two dimensions in the spatial domain and one in the temporal domain, and we propose a despeckling algorithm for dynamic ultrasound named the 3-D Gabor-based anisotropic diffusion (GAD-3D). The GAD-3D expands the classic two-dimensional Gabor-based anisotropic diffusion (GAD) into 3-D domain. First, we proposed a robust 3-D Gabor-based edge detector by capturing the edge with 3-D Gabor transformation. Then we embed this novel detector into the partial differential equation of GAD to guide the 3-D diffusion process. In the simulation experiment, when the noise variance is as high as 0.14, the GAD-3D improves the Pratt’s figure of merit, mean structural similarity index and peak signal-to-noise ratio by 24.32%, 10.98%, and 6.51%, respectively, compared with the best values of seven other methods. Experimental results on clinical dynamic ultrasonography suggest that the GAD-3D outperforms the other seven methods in noise reduction and detail preservation. The GAD-3D is effective for dynamic ultrasound despeckling and may be potentially valuable for disease assessment in dynamic medical ultrasonography

3-D Gabor-based anisotropic diffusion for speckle noise suppression in dynamic ultrasound images

Speckle noise contaminates medical ultrasound images, and the suppression of speckle noise is helpful for image interpretation. Traditional ultrasound denoising (i.e., despeckling) methods are developed on two-dimensional static images. However, one of the advantages of ultrasonography is its nature of dynamic imaging. A method for dynamic ultrasound despeckling is expected to incorporate both the spatial and temporal information in successive images of dynamic ultrasound and thus yield better denoising performance. Here we regard a dynamic ultrasound video as three-dimensional (3-D) images with two dimensions in the spatial domain and one in the temporal domain, and we propose a despeckling algorithm for dynamic ultrasound named the 3-D Gabor-based anisotropic diffusion (GAD-3D). The GAD-3D expands the classic two-dimensional Gabor-based anisotropic diffusion (GAD) into 3-D domain. First, we proposed a robust 3-D Gabor-based edge detector by capturing the edge with 3-D Gabor transformation. Then we embed this novel detector into the partial differential equation of GAD to guide the 3-D diffusion process. In the simulation experiment, when the noise variance is as high as 0.14, the GAD-3D improves the Pratt’s figure of merit, mean structural similarity index and peak signal-to-noise ratio by 24.32%, 10.98%, and 6.51%, respectively, compared with the best values of seven other methods. Experimental results on clinical dynamic ultrasonography suggest that the GAD-3D outperforms the other seven methods in noise reduction and detail preservation. The GAD-3D is effective for dynamic ultrasound despeckling and may be potentially valuable for disease assessment in dynamic medical ultrasonography