Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods

2019 ◽  
Vol 115 ◽  
pp. 379-388 ◽  
Author(s):  
Benjamin A. Young ◽  
Alex Hall ◽  
Laurent Pilon ◽  
Puneet Gupta ◽  
Gaurav Sant
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Statistical Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Compressive Strength Of Concrete

Data in Construction Grammar

2013 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Computational Linguistic ◽  
Statistical Analysis ◽  
Construction Grammar ◽  
Data Types ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
New Directions ◽  
Corpus Data

This chapter examines the types of data used in constructionist approaches and the parameters along which data types can be classified. It discusses different kinds of quantitative observational/corpus data (frequencies, probabilities, association measures) and their statistical analysis. In addition, it provides a survey of a variety of different experimental data (novel word/construction learning, priming, sorting, etc.). Finally, the chapter discusses computational-linguistic/machine-learning methods as well as new directions for the development of new data and methods in Construction Grammar.

High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods

2020 ◽  
pp. 118-140
Author(s):  
Melda Yucel ◽  
Ersin Namlı
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Prediction Model ◽  
High Performance Concrete ◽  
Prediction Method ◽  
Structural Features ◽  
Strength Prediction ◽  
Concrete Compressive Strength ◽  
Learning Methods ◽  
Machine Learning Methods

In this chapter, prediction applications of concrete compressive strength values were realized via generation of various hybrid models, which are based on decision trees as main prediction method, by using different artificial intelligence and machine learning techniques. In respect to this aim, a literature research was presented. Used machine learning methods were explained together with their developments and structural features. Various applications were performed to predict concrete compressive strength, and then feature selection was applied to prediction model in order to determine primarily important parameters for compressive strength prediction model. Success of both models was evaluated with respect to correct and precision prediction of values with different error metrics and calculations.

scholarly journals Data-driven monitoring of the gearbox using multifractal analysis and machine learning methods

2019 ◽  
Vol 252 ◽  
pp. 06006
Author(s):  
Andrzej Puchalski ◽  
Iwona Komorska
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Analysis ◽  
Multifractal Analysis ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wavelet Leaders

Data-driven diagnostic methods allow to obtain a statistical model of time series and to identify deviations of recorded data from the pattern of the monitored system. Statistical analysis of time series of mechanical vibrations creates a new quality in the monitoring of rotating machines. Most real vibration signals exhibit nonlinear properties well described by scaling exponents. Multifractal analysis, which relies mainly on assessing local singularity exponents, has become a popular tool for statistical analysis of empirical data. There are many methods to study time series in terms of their fractality. Comparing computational complexity, a wavelet leaders algorithm was chosen. Using Wavelet Leaders Multifractal Formalism, multifractal parameters were estimated, taking them as diagnostic features in the pattern recognition procedure, using machine learning methods. The classification was performed using neural network, k-nearest neighbours’ algorithm and support vector machine. The article presents the results of vibration acceleration tests in a demonstration transmission system that allows simulations of assembly errors and teeth wear.

scholarly journals A comparison of machine learning methods for predicting the compressive strength of field-placed concrete

2019 ◽  
Vol 228 ◽  
pp. 116661 ◽  
Author(s):  
M.A. DeRousseau ◽  
E. Laftchiev ◽  
J.R. Kasprzyk ◽  
B. Rajagopalan ◽  
W.V. Srubar
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Crystals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 779
Author(s):  
Muhammad Nasir Amin ◽  
Ammar Iqtidar ◽  
Kaffayatullah Khan ◽  
Muhammad Faisal Javed ◽  
Faisal I. Shalabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Harmful Effect ◽  
Partial Replacement ◽  
Learning Methods ◽  
Traditional Methods ◽  
Inference System ◽  
Machine Learning Methods

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

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