Meso Analysis on Soft Soil Uniaxial Consolidation Experiment with CT

2012 ◽  
Vol 204-208 ◽  
pp. 539-544
Author(s):  
Ling Tao Mao ◽  
Dan Zhao ◽  
Kai Zhou ◽  
Ze Xun Yuan ◽  
Ji Li An
Keyword(s):  
Soil Sample ◽  
Soft Soil ◽  
Ct Images ◽  
Soil Samples ◽  
Ct Image ◽  
Pressure Loading ◽  
Consolidation Process ◽  
Marine Deposits ◽  
Structure Changes ◽  
The Relationship

In this paper, marine sediments soft soil of Beigangchi wharf in Tianjin port was scanned by Computer Tomography(CT) in different load during uniaxial consolidation experiment. The CT images were analyzed to research on the relationship between the microcosmic characteristics of Marine deposits soil and it’s compression in Tianjin areas. The results show that: with the increase of the pressure loading, the average grey value of the CT images increases gradually, which illustrates that soil samples are compacted and the density increases. The variance decreasing of CT image grey value indicates that the soil sample get more evenly. Soft soil of Beigangchi wharf in Tianjin port has high compressibility through the changes of grey compression coefficient. This paper can be referenced for the research of the structure changes of the soil sample consolidation process.

Architecture Concrete Mesostructure Analysis Based on CT Image Fracture Process

2011 ◽  
Vol 368-373 ◽  
pp. 2638-2641
Author(s):  
Liang Zhao ◽  
Chang Hua Li ◽  
Fa Ning Dang ◽  
Deng Feng Chen
Keyword(s):  
Spatial Distribution ◽  
Volume Expansion ◽  
Fracture Process ◽  
Ct Images ◽  
Spatial Distributions ◽  
Ct Image ◽  
Destruction Process ◽  
Ct Data ◽  
The Relationship ◽  
Variation Process

Scanning observation on meso evolution of fracture in concrete is carried out by means of computerized tomography (CT) on uniaxial compressive condition. The cracks in the mortar expansion, in particular, the bond of mortar and aggregate which is key regions of concrete damaged, are drawn out through CT image and CT data, and the destruction process of the concrete can be divided into four stakes, compression, enlargement, the expansion of the CT crack,and destruction. According to the character of CT image，MMD is used to analyze the CT images of the concrete specimens in 4 stages of deformation. The components of the CT images are classified and the spatial distributions of crack or cavity, mortar and aggregate are obtained. The variation process of the relationship between distributions of crack or cavity magnitude and stress are obtained from classification maps. The specimens experienced the process of condensed, volume expansion, crack propagation, coalescence and failure. The method can not only reflect the spatial distribution of the materials but also simplify the following analyses that follow.

scholarly journals Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Daryl L. X. Fung ◽  
Qian Liu ◽  
Judah Zammit ◽  
Carson Kai-Sang Leung ◽  
Pingzhao Hu
Keyword(s):  
Deep Learning ◽  
Causal Relationship ◽  
Learning Model ◽  
Ct Images ◽  
Ct Image ◽  
Underlying Diseases ◽  
Relationship Of ◽  
The Relationship ◽  
Deep Learning Model ◽  
Lung Ct

Abstract Background Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. Methods In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. Results The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. Conclusions This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases).

Soft Soil Microstructure Character Analysis with CT

2011 ◽  
Vol 90-93 ◽  
pp. 588-592
Author(s):  
Ze Xun Yuan ◽  
Ling Tao Mao ◽  
Dan Zhao ◽  
Zhen Yu Chi
Keyword(s):  
Soft Soil ◽  
Ct Images ◽  
Ct Image ◽  
Character Analysis ◽  
Mercury Intrusion ◽  
Soil Microstructure ◽  
Ct Technique ◽  
Electronic Microscope

In this paper, CT technique is applied to observe soft soil of different depth , and microstructure characters are analyzed combined with scanning electronic microscope (SEM) and mercury intrusion method. CT images comprehensively reflect the soil microstructure, while every voxel of CT image can be observed with SEM. CT images grey value can reflect the porosity variance, and grey value variance express homogeneity of soil microstructure

scholarly journals Study on the effect of samplers on the disturbance of dredger-filled soil structure by numerical simulation layering method

2019 ◽  
Vol 131 ◽  
pp. 01060
Author(s):  
Zhijie Li ◽  
Jing Song ◽  
Zhou Zhao ◽  
Shouying Yang ◽  
Jiansen Huang ◽  
...  
Keyword(s):  
Soil Sample ◽  
Displacement Field ◽  
Inflection Point ◽  
Dynamic Properties ◽  
Soft Soil ◽  
Soil Samples ◽  
Particle Displacement ◽  
Soil Layers ◽  
Bending Deformation ◽  
Inflection Points

Through the use of the particle flow software simulation, the influences on the microscopic dynamic properties of high viscosity soft soil caused by the inner diameter, cut angle and wall thickness of the samplers were studied with the example of the dredged mud. The motion behavior of the inflection point of particle characteristics was compared in seven soil samples by dividing the soil layers. The results show that a convective displacement field of the soil particles is formed at the bottom, and the particles above the convection are mainly subject to tensile expansion while the particles below the convection are mainly compressed and contracted, which result in the regular changes in porosity and bending deformation of the soil layers. There is a synergistic relationship between the intersection of the porosity curve and the initial porosity curve and the convective position of the particle displacement field. There is a characteristic inflection point in the interlayer particle displacement with the soil layers behave as a bending deformation. The particle disturbance between the inflection points is not obvious and the particle disturbance outside the inflection points is larger. The deformation feature can be fitted to a rotating paraboloid with the lower opening and the disturbance of the seven groups soil samples can be assessed initially by the a value of the surface equation. The line connecting the inflection points of the interlayer features can be refitted to a paraboloid of rotation. The ratio of the volume of the paraboloid to the volume of the soil sample can be used to evaluate the originality of the soil sample.

scholarly journals Relationship between solitary pulmonary nodule lung cancer and CT image features based on gradual clustering

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 400-404
Author(s):  
Weipeng Zhang
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Image Features ◽  
Ct Image ◽  
Lung Cancers ◽  
Diagnosis Rate ◽  
Malignant Lesions ◽  
The Relationship

Abstract Background The relationship between the medical characteristics of lung cancers and computer tomography (CT) images are explored so as to improve the early diagnosis rate of lung cancers. Methods This research collected CT images of patients with solitary pulmonary nodule lung cancer, and used gradual clustering methodology to classify them. Preliminary classifications were made, followed by continuous modification and iteration to determine the optimal condensation point, until iteration stability was achieved. Reasonable classification results were obtained. Results the clustering results fell into 3 categories. The first type of patients was mostly female, with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, with pleural indentation; The second type of patients was mostly male with ages between 50 and 80 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, but with no pleural indentation; The third type of patients was also mostly male with ages between 50 and 80 years. CT images for this group showed no abnormalities. Conclusions the application of gradual clustering methodology can scientifically classify CT image features of patients with lung cancer in the initial lesion stage. These findings provide the basis for early detection and treatment of malignant lesions in patients with lung cancer.

scholarly journals High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting—implications for sediment fingerprinting

2021 ◽  
Author(s):  
Niels F. Lake ◽  
Núria Martínez-Carreras ◽  
Peter J. Shaw ◽  
Adrian L. Collins
Keyword(s):  
Particle Size ◽  
Soil Sample ◽  
Suspended Sediment ◽  
Temporal Frequency ◽  
Soil Samples ◽  
Sediment Sources ◽  
High Temporal Frequency ◽  
Novel Method ◽  
The Relationship

Abstract Purpose This study tests the feasibility of using a submersible spectrophotometer as a novel method to trace and apportion suspended sediment sources in situ and at high temporal frequency. Methods Laboratory experiments were designed to identify how absorbance at different wavelengths can be used to un-mix artificial mixtures of soil samples (i.e. sediment sources). The experiment consists of a tank containing 40 L of water, to which the soil samples and soil mixtures of known proportions were added in suspension. Absorbance measurements made using the submersible spectrophotometer were used to elucidate: (i) the effects of concentrations on absorbance, (ii) the relationship between absorbance and particle size and (iii) the linear additivity of absorbance as a prerequisite for un-mixing. Results The observed relationships between soil sample concentrations and absorbance in the ultraviolet visible (UV–VIS) wavelength range (200–730 nm) indicated that differences in absorbance patterns are caused by soil-specific properties and particle size. Absorbance was found to be linearly additive and could be used to predict the known soil sample proportions in mixtures using the MixSIAR Bayesian tracer mixing model. Model results indicate that dominant contributions to mixtures containing two and three soil samples could be predicted well, whilst accuracy for four-soil sample mixtures was lower (with respective mean absolute errors of 15.4%, 12.9% and 17.0%). Conclusion The results demonstrate the potential for using in situ submersible spectrophotometer sensors to trace suspended sediment sources at high temporal frequency.

scholarly journals Fluka simulation of PGNAA system for determining heavy metal pollution in the soil sample

Nukleonika ◽  
2020 ◽  
Vol 65 (1) ◽  
pp. 13-17
Author(s):  
Can Zhang ◽  
Jianbo Yang ◽  
Rui Li ◽  
Yujie Qiao ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Soil Sample ◽  
Soil Samples ◽  
Prompt Gamma ◽  
Soil Thickness ◽  
Source Distance ◽  
Γ Ray ◽  
Gamma Neutron Activation ◽  
The Relationship

AbstractThis study presented a self-designed prompt gamma neutron activation analysis (PGNAA) model and used Fluka simulation to simulate the heavy metals (Mn, Cu, Hg, Ni, Cr, Pb) in soil samples. The relationship between the prompt γ -ray yield of each heavy metal and soil thickness, content of heavy metals in the soil, and source distance was obtained. Simulation results show that the prompt γ -ray yield of each heavy metal increases with the increase in soil thickness and reaches saturation at 18 cm. The greater the proportion of heavy metals in the soil, the greater the prompt γ -ray yield. The highest content is approximately 3%, and the change in distance between the neutron source and soil sample does not affect the prompt γ -ray yield of heavy metals.

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

KEBERADAAN BAKTERI PELARUT FOSFAT DAN AKTIVITAS ENZIM FOSFATASE TANAH DAERAH PERAKARAN TANAMAN OBAT DARI KEBUN RAYA CIBODAS

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Suliasih Suliasih
Keyword(s):  
Alkaline Phosphatase ◽  
Soil Sample ◽  
Rhizosphere Soil ◽  
Botanical Garden ◽  
Soil Enzyme ◽  
Soil Samples ◽  
Phosphate Solubilizing Bacteria ◽  
Bacillus Sp ◽  
Phosphate Solubilizing ◽  
Serratia Rubidaea

A study was undertaken to investigate to occurance of phosphate solubilizing bacteria from rhizosphere soil samples of medicine plants in Cibodas Botanical Garden. 13 soil samples of medicine plants are collected randomly The result shows that 71 isolates of phosphate solubilizing bacteria were isolated, and 10 species of these organism was identified as Azotobacter sp, Bacillus sp, Chromobacterium sp, C.violaceum, Citrobacter sp. , Enterobacter sp., E. liquefaciens. Nitrosomonas sp., Serratia rubidaea, Sphaerotillus natans. Azotobacter sp. And Bacillus sp. Are found in all of soil tested. Conversely, Serratia rubidaea is only in the sample from rhizosphere of Plantago mayor The activity of acid alkaline phosphatase in soil tested ranged from 0.78 – 60,18 ugp nitrophenole/g/h, with the higest values being recorded in soil sample from rhizosphere of “Lavender”.Keywords : phosphate solubilizing bacteria, soil enzyme phosphatase

Evaluation of Nanotechniques and Conventional Techniques for the Removal of Dioxins

2018 ◽  
Vol 9 (1) ◽  
pp. 79-84
Author(s):  
Vaishali V. Shahare ◽  
Rajni Grover ◽  
Suman Meena
Keyword(s):  
Soil Sample ◽  
Human Health ◽  
Surface Area ◽  
Contaminated Soil ◽  
Conventional Treatment ◽  
Volume Ratio ◽  
Soil Samples ◽  
Dioxins And Furans ◽  
Palladized Iron ◽  
The Impact

Background: The persistent dioxins/furans has caused a worldwide concern as they influence the human health. Recent research indicates that nonmaterial may prove effective in the degradation of Dioxins/furans. The nanomaterials are very reactive owing to their large surface area to volume ratio and large number of reactive sites. However, nanotechnology applications face both the challenges and the opportunities to influence the area of environmental protection. Objective: i) To study the impact of oil mediated UV-irradiations on the removal of 2,3,7,8-TCDD, 2,3,7,8-TCDF, OCDD and OCDF in simulated soil samples. ii) To compare the conventional treatment methods with the modern available nanotechniques for the removal of selected Dioxins/furans from soil samples. Methods: The present work has investigated an opportunity of the degradation of tetra and octachlorinated dioxins and furans by using oil mediated UV radiations with subsequent extraction of respective dioxins/furans from soils. The results have been compared with the available nanotechniques. Results: The dioxin congeners in the simulated soil sample showed decrease in concentration with the increase in the exposure time and intensity of UV radiations. The dechlorination of PCDD/Fs using palladized iron has been found to be effective. Conclusion: Both the conventional methods and nanotechnology have a dramatic impact on the removal of Dioxins/furans in contaminated soil. However, the nanotechniques are comparatively costlier and despite the relatively high rates of PCDDs dechlorination by Pd/nFe, small fraction of the dioxins are recalcitrant to degradation over considerable exposure times.

Sign in / Sign up

Export Citation Format

Share Document