PDF Review of Rock Properties Based on Drilling Parameters

index for prediction of penetration rate of rotary drilling. They reached some good correlations between drillability index and compressive strength, tensile strength, point load index, Schmidt Hammer rebound, impact strength, P-wave velocity, elastic modulus and density. Kahraman (2002) further found that the

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Enhanced soft computing for ensemble approach to estimate

The results recommend that ANN model is more accurate to predict the compressive strength as compare to GP and SVM based models. Sensitivity analysis indicated that Cement (C), Silica fume (SF), Fly ash (FA) and Water (W) are the most valuable constituents in which compressive strength of the HCS is mainly depend for this data set.

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Frontiers | Prediction of Rubber Fiber Concrete Strength

The training time of extreme learning and support vector machine is similar, and both are shorter than the BP neural network. Jian Tang et al. compared several concrete compressive strength prediction model methods based on extreme learning machines (ELMs) (Tang et al., 2014). The results show that the conventional ELMs algorithm has fast

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Prediction of Compressive Strength of Concrete and Rock

The use of machine learning techniques to predict material strength is becoming popular. However, not much attention has been paid to instance-based learning (IBL) algorithms. Therefore, in order to predict material strength, as the direct method by conducting tests is time-consuming and expensive and experimental errors are inevitable, an indirect method based on elementary instance

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Random Forest versus Support Vector Machine Models

It is important to improve the predictive performance during the design of RC beams to enable the accurate prediction of the shear strength of different types of RC beams. Several proposals have been made in recent years regarding the use of advanced machine learning (ML) models for shear strength prediction [13, 14].

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IOP Conference Series: Materials Science and Engineering

The potential application of this approach in the context of serial body-shop production improves the prediction of joining quality and the process availability significantly. A new machine learning based method for sampling virtual experiments and its effect on the Dimensionally accurate parts made of high-strength steels - compressive

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Artificial intelligence for the compressive strength

Engineered Geopolymer Composites has proved to be an excellent eco-friendly strain hardening composite materials, as well as, it exhibits high tensile strain capacity. An intelligent computing tool based predictive model to anticipate the compressive strength of ductile geopolymer composites would help various researchers to analyse the material type and its contents; the dosage of fibers

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Comprehensive Machine Learning-Based Model for

Compressive Strength of Ready-Mix Concrete Jiajia Xu, Li Zhou, Ge He, Xu Ji * , Yiyang Dai and Yagu Dang is a trend to employ data-driven techniques for concrete CS prediction. Compared with This paper proposes a machine learning-based predictive model that integrates a genetic algorithm (GA) and random forest (RF) to comprehensively

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Model the compressive strength of high ... - Neural Designer

Compressive strength is one of the most important properties of concrete. It is measured by breaking cylindrical concrete specimens in a compression-testing machine. The objective of this example is to design concrete mixtures with specified properties and reduced costs. To do that, a compressive strength's predictive model is built from a set

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Compressive Strength of Unidirectional Composites

Abstract. The present work examines the effect of resin ductility (varied as a function of temperature) on the compressive strength of unidirectional T800/924C carbon fibre-epoxy laminates. Tests are conducted in a screw-driven machine between room temperature and 100°C. Untabbed straight-sided specimens are used in a modified Celanese test

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Geological Strength Index Prediction by Vision and Machine

In this study, we propose a novel methodology in order to predict Geological Strength Index (GSI) values of rock outcrops by using computer vision and machine learning methods. For this purpose, we separately employed two different global image descriptors namely GIST and HOG (Histogram of Oriented Gradients) to extract a discriminative and

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An ensemble tree-based machine learning model for

Estimating the uniaxial compressive strength (UCS) of travertine rocks with an indirect modeling approach and machine learning algorithms is useful as models can reduce the cost and time required to obtain accurate measurements of UCS, which is

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PDF Design of Alkali Activated Slag‒Fly Ash Concrete Mixes

alkali activated concrete. The machine learning-based classifiers were engaged for the strength predictions and the results illustrated that Si/Al ratio is the most significant parameter followed by Al/Na ratio. Machine learning-based classifiers were able to predict the compressive strength with high precision.

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PDF) Anti-Phishing Technology using New compression based

Machine learning based text classification expected to occur frequently in the data. In this study, we typically consists of four steps: built two word-dictionaries, which consist of common words in phishing and non-phishing websites respectively.

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PDF Development of predictive models for shear strength of HSC

Development of predictive models for shear strength of HSC slender beams without web reinforcement using machine-learning based techniques A. Kaveha;, T. Bakhshpoorib, and S.M. Hamze-Ziabaric a. Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, Tehran, P.O. Box 16846-13114

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PDF Relationship between the ECT-strength of corrugated board

The compression strength of any corrugated board box is a direct measure of its stacking strength. The compression strength of a box is measured according to a standardized test method known as the Box Crush Test. Numerous studies, initiated from the basic work conducted by McKee and Gander (1962), have been

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On the Use of Machine Learning Models for Prediction of

Compared with traditional prediction methods, an advantage of machine learning is that the prediction could be made without knowing the exact relationship between features and compressive strength. Machine learning models have been used for predicting compres-sive strength of concrete for a long time [19,20]. Different ML models, from simple linear

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A Data-Driven Machine Learning Approach to Predict the

A total of 3800 data points were collected from different published sources covering a wide range of input parameters. Moreover, explicit empirical correlations are also derived that can be used explicitly without the need for any machine learning-based software.

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Real-Time Prediction of Litho-Facies From Drilling Data

Rock Strength Prediction in Real-Time While Drilling Employing Random Forest and Functional Network Techniques and air conditioning system based on two-phase ejector driven by exhaust gases of natural gas fueled homogeneous charge compression ignition engine. J. Energy Resour. Machine Learning-Based Improved Pressure-Volume

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Acquisition of Channel State Information for mmWave

Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches. 06/16/2020 ∙ by Chenhao Qi, et al. ∙ 0 ∙ share . The accuracy of available channel state information (CSI) directly affects the performance of millimeter wave (mmWave) communications.

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Machine Learning Enables Prompt Prediction of Hydration

This paper introduces a machine learning-based methodology to enable prompt and high-fidelity prediction of time-dependent hydration kinetics of cement, both in plain and multicomponent (e.g., binary; and ternary) systems, using the system’s physicochemical characteristics as inputs.

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