scholarly journals Predicting Compressive Strength of Cement-Stabilized Rammed Earth Based on SEM Images Using Computer Vision and Deep Learning

2019 ◽  
Vol 9 (23) ◽  
pp. 5131 ◽  
Author(s):  
Piotr Narloch ◽  
Ahmad Hassanat ◽  
Ahmad S. Tarawneh ◽  
Hubert Anysz ◽  
Jakub Kotowski ◽  
...  
Keyword(s):  
Computer Vision ◽  
Compressive Strength ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Image Database ◽  
Rammed Earth ◽  
Sem Images ◽  
Current Testing ◽  
Sem Image ◽  
Reasonable Prediction

Predicting the compressive strength of cement-stabilized rammed earth (CSRE) using current testing machines is time-consuming and costly and may harm the environment due to the samples’ waste. This paper presents an automatic method using computer vision and deep learning to solve the problem. For this purpose, a deep convolutional neural network (DCNN) model is proposed, which was evaluated on a new in-house scanning electron microscope (SEM) image database containing 4284 images of materials with different compressive strengths. The experimental results show reasonable prediction results compared to other traditional methods, achieving 84% prediction accuracy and a small (1.5) oot Mean Square Error (RMSE). This indicates that the proposed method (with some enhancements) can be used in practice for predicting the compressive strength of CSRE samples.

scholarly journals The Effect of Soil Mineral Composition on the Compressive Strength of Cement Stabilized Rammed Earth

Materials ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 324 ◽  
Author(s):  
Piotr Narloch ◽  
Piotr Woyciechowski ◽  
Jakub Kotowski ◽  
Ireneusz Gawriuczenkow ◽  
Emilia Wójcik
Keyword(s):  
Compressive Strength ◽  
Mineral Composition ◽  
Cement Content ◽  
Rammed Earth ◽  
Compression Tests ◽  
Soil Mineral ◽  
Sem Images ◽  
Mineral Compositions ◽  
Strength Result ◽  
Distribution Water

Cemented stabilized rammed earth (CSRE) is a building material used to build load bearing walls from locally available soil. The article analyzes the influence of soil mineral composition on CSRE compressive strength. Compression tests of CSRE samples of various mineral compositions, but the same particle size distribution, water content, and cement content were conducted. Based on the compression strength results and analyzed SEM images, it was observed that even small changes in the mineral composition significantly affected the CSRE compressive strength. From the comparison of CSRE compressive strength result sets, one can draw general qualitative conclusions that montmorillonite lowered the compressive strength the most; beidellite also lowered it, but to a lesser extent. Kaolinite lightly increased the compressive strength.

Effect of Glutinous Rice Paste on Compressive Strength of Lime-Stabilized Soil

2011 ◽  
Vol 280 ◽  
pp. 5-8
Author(s):  
Hong Tao Peng ◽  
Qi Zhang ◽  
Nai Sheng Li ◽  
De Fa Wang
Keyword(s):  
Compressive Strength ◽  
Soil Sample ◽  
Experimental Results ◽  
Box Dimension ◽  
Sem Images ◽  
Sem Image ◽  
Glutinous Rice ◽  
Stabilized Soil ◽  
The Difference ◽  
The One

The lime-stabilized soil was mixed with glutinous rice paste in proper proportion to determine the difference in compressive strength caused by introduction of glutinous rice paste. The experimental results show that the unconfined compressive strengths of lime-stabilized soil specimens treated with glutinous rice paste are all higher than those without treated at different curing times (7d, 28d, 40d, and 60d). The calculated fractal box dimension value of SEM image of lime stabilized soil sample is close to and slightly less than the one treated with glutinous rice paste. The SEM images show that the microstructure of lime-stabilized soil treated with glutinous rice paste is denser than that without treated. This kind of denser microstructure should be the basis of higher unconfined compressive strengths of the specimens treated with glutinous rice paste.

scholarly journals Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

2020 ◽  
Vol 190 ◽  
pp. 108541 ◽  
Author(s):  
Brian Gallagher ◽  
Matthew Rever ◽  
Donald Loveland ◽  
T. Nathan Mundhenk ◽  
Brock Beauchamp ◽  
...  
Keyword(s):  
Computer Vision ◽  
Compressive Strength ◽  
Deep Learning ◽  
Molecular Solids

scholarly journals Prediction of Mechanical Strength Based on Deep Learning Using the Scanning Electron Image of Microscopic Cemented Paste Backfill

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xuebin Qin ◽  
Shifu Cui ◽  
Lang Liu ◽  
Pai Wang ◽  
Mei Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mechanical Strength ◽  
Mechanical Response ◽  
Cemented Paste Backfill ◽  
Sem Images ◽  
Important Indicator ◽  
Sem Image ◽  
Paste Backfill ◽  
The Difference ◽  
Scanning Electron

The mechanical strength of cemented backfill is an important indicator in mining filling. To study the nonlinear relationship between cemented paste backfill (CPB) and mechanical response, a deep learning technique is employed to establish the end-to-end mapping relationship between the scanning electron microscope (SEM) images and mechanical strength. A seven-layer convolution neural network is set up in the experiment, and the relationship between the SEM image and mechanical strength is established. In addition, the difference between the measured and predicted values is calculated and the mean and variance of the error are analyzed. The average accuracy of the mechanical strength prediction is found to be 8.28%. Thus, the proposed method provides a new technique for the quantitative analysis of mechanical strength of microscale CPB.

scholarly journals Topological Distance-Based Electron Interaction Tensor: A Novel Molecular Structure Representation to Bridge Convolutional Neural Network Studies in Computer Vision to Drug-Like Compound Datasets

2021 ◽  
Author(s):  
Hyun Kil Shin
Keyword(s):  
Neural Network ◽  
Molecular Structure ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Prediction Accuracy ◽  
Chemical Data ◽  
Electron Interaction ◽  
Learning Models ◽  
Topological Distance

Abstract Owing to the success achieved by deep learning, researchers are exploringthe application of deep learning in drug discovery to improve the accuracy of prediction models. Significant performance improvement has been achieved by diverse convolutional neural network (CNN) models in computer vision, and the preparation of an input format suitable for CNN is one of the major questions required to be answered in order to harness the advancements in using CNNs for chemical data. It was reported that the models achieved improvement in prediction accuracy, in deep learning studies on molecular structure data; however, the improvement was insufficient from an industry perspective. Furthermore, a recent study suggested that conventional machine learning models can outperform deep learning models on chemical data. As only a limited number of feature calculation methods are available for molecules in deep learning studies, it is crucial to develop more methods to calculate features appropriate for deep learning model development.A topological distance-based electron interaction (TDEi) tensor has been introduced in this study to transform a molecular structure into image-like 3D arrays based on electron interactions (Eis) within a molecule. The prediction accuracy of the CNN model with the TDEi tensor was tested with four datasets: MP (275,131), Lipop (4,193), Esol (1,127), and Freesolv (639), and the models achieved desirable prediction accuracy. Ei is the fundamental level of information that determines the chemical properties of a molecule. Feature space variation was visualized by taking outputs from the middle of the CNN architecture as the CNN model exhibited outstanding performance in automatic feature extraction.The correlation between features from the CNN, and target endpoints was strengthened as outputs were extracted from the deeper layer of the CNN.

scholarly journals Tensor Methods in Computer Vision and Deep Learning

2021 ◽  
Vol 109 (5) ◽  
pp. 863-890
Author(s):  
Yannis Panagakis ◽  
Jean Kossaifi ◽  
Grigorios G. Chrysos ◽  
James Oldfield ◽  
Mihalis A. Nicolaou ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Tensor Methods

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Effects of carbon nanotubes on expanded glass and silica aerogel based lightweight concrete

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suman Kumar Adhikary ◽  
Žymantas Rudžionis ◽  
Simona Tučkutė ◽  
Deepankar Kumar Ashish
Keyword(s):  
Carbon Nanotubes ◽  
Compressive Strength ◽  
Silica Aerogel ◽  
Lightweight Concrete ◽  
Lightweight Aggregate ◽  
Cementitious Materials ◽  
Lightweight Aggregate Concrete ◽  
Multi Wall Carbon Nanotubes ◽  
Sem Image ◽  
Study Results

AbstractThis study is aimed to investigate the effect of carbon nanotubes on the properties of lightweight aggregate concrete containing expanded glass and silica aerogel. Combinations of expanded glass (55%) and hydrophobic silica aerogel particles (45%) were used as lightweight aggregates. Carbon nanotubes were sonicated in the water with polycarboxylate superplasticizer by ultrasonication energy for 3 min. Study results show that incorporating multi-wall carbon nanotubes significantly influences the compressive strength and microstructural performance of aerogel based lightweight concrete. The addition of carbon nanotubes gained almost 41% improvement in compressive strength. SEM image of lightweight concrete shows a homogeneous dispersal of carbon nanotubes within the concrete structure. SEM image of the composite shows presence of C–S–H gel surrounding the carbon nanotubes, which confirms the cites of nanotubes for the higher growth of C–S–H gel. Besides, agglomeration of carbon nanotubes and the presence of ettringites was observed in the transition zone between the silica aerogel and cementitious materials. Additionally, flowability, water absorption, microscopy, X-ray powder diffraction, and semi-adiabatic calorimetry results were analyzed in this study.

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