The determination of ground granulated concrete compressive strength based machine learning models

The advancement of machine learning (ML) models has received remarkable attention by several science and engineering applications. Within the material engineering, ML models are usually utilized for building an expert system for supporting material design and attaining an optimal formulation material sustainability and maintenance. The current study is conducted on the based of the utilization of ML models for modeling compressive strength (Cs) of ground granulated blast furnace slag concrete (GGBFSC). Random Forest (RF) model is developed for this purpose. The predictive model is constructed based on multiple correlated properties for the concrete material including coarse aggregate (CA), curing temperature (T), GGBFSC to total binder ratio (GGBFSC/B), water to binder ratio (w/b), water content (W), fine aggregate (FA), superplasticizer (SP). A total of 268 experimental dataset are gather form the open-source previous published researches, are used to build the predictive model. For the verification purpose, a predominant ML model called support vector machine (SVM) is developed. The efficiency of the proposed predictive and the benchmark models is evaluated using statistical formulations and graphical presentation. Based on the attained prediction accuracy, RF model demonstrated an excellent performance for predicting the Cs using limited input parameters. Overall, the proposed methodology showed an exceptional predictive model that can be utilized for modeling compressive strength of GGBFSC

ANALITICAL STUDY ON COMPRESISVE STRENGTH OF REACTIVE POWDER CONCRETE

This work focuses on development of Artificial Neural Networks (ANNs) in prediction of compressive strength of reactive powder concrete after 28 days. To predict the compressive strength of reactive powder concrete nine input parameters that are cement, water, silica fume, fly ash, Ground granulated blast Furnace slag, super plasticizer, fine aggregate, Quartz sand and steel fibres are identified. A total of 35 different data sets of concrete were collected from the technical literatures. Number of layers, number of neurons, activation functions were considered and the results were validated using an independent validation data set. A detailed study was carried out, considering single hidden layers for the architecture of neural network. The performance of the 9-3-1 architecture was the best possible architecture. The results of the present investigation indicate that ANNs have strong potential as a feasible tool for predicting the compressive strength of reactive powder concrete

MAT-701: PREDICTING THE COMPRESSIVE STRENGTH OF ULTRA-LIGHTWEIGHT CONCRETE BY AN ARTIFICIAL NEURAL NETWORK

Ultra-lightweight concrete (ULWC) has potential applications for floating structures and architectural elements because of its dry density coming in at under 1000 kg/m3. The objective was to develop an artificial neural network (ANN) to aid the ULWC designer according to his needs. Boundary conditions were set for each material and 13 constraints based on the water binder ratio, density, air content, binder and aggregate content. The ANN predicted the compressive strength with a comfortable margin of error, with the gap encountered being attributed to variability in workability. Precise constraints and boundary conditions are needed to ensure a lower variability in workability. The ANN, coupled with a genetic algorithm, can generate millions of mixes for a given compressive strength in a short amount of time. The designer is able to choose mixes according to additional needs, such as the carbon footprint, absolute density, polymer content, cost, etc

Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inferior to OPC in design-governing functional properties. Hence, concrete manufacturers and scientists have been seeking methods to predict the performance of BCC mixes in order to reduce the cost and time of experimentally testing all alternatives. Machine learning algorithms have been proven in other fields for treating large amounts of data drawing meaningful relationships between data accurately. However, the existing prediction models in the literature come short in covering a wide range of SCMs and/or functional properties. Considering this, in this study, a non-linear multi-layered machine learning regression model was created to predict the performance of a BCC mix for slump, strength, and resistance to carbonation and chloride ingress based on any of five prominent SCMs: fly ash, ground granulated blast furnace slag, silica fume, lime powder and calcined clay. A database from>150 peer-reviewed sources containing>1650 data points was created to train and test the model. The statistical performance was found to be comparable to that of existing models (R = 0.94–0.97). For the first time, the model, Pre-bcc, was also made available online for users to conduct their own prediction studies.Peer ReviewedPostprint (published version

Behaviour of ground cupola furnace slag blended concrete at elevated temperature

Fires adversely affect the performance of concrete when expose to extreme temperatures. However, it is important to study the effects of elevated temperature on the concrete properties. Concrete often contains other cementitious materials such as ground granulated blast furnace slag (GGBFS) and this has been successfully used to improve its properties. Hence, little or no study has been carried out on the use of ground cupola furnace slag (GCFS) in concreting. Therefore, this paper investigates the behavior of concrete blended with GCFS at elevated temperatures. A total of 300 samples were prepared with four different GCFS contents. The test specimens were cured for 28-d and 56-d and subjected to elevated temperatures ranging from 200oC to 800oC up to 24 h. The slump, residual compressive and tensile strength tests were carried out on fresh and hardened concrete. The results showed that the compressive strength and splitting tensile strengths of concrete generally increased with increasing % GCFS content but decreased as temperature increases. At 28-d and 56-d, the strengths were observed to be maximum at 10% replacement when the temperature is 200oC compared to other mixes. It can be concluded that the strength drastically decreased at temperature above 200oC. An analysis of variance (ANOVA) was also carried out to determine the effect of the elevated temperature and percentage replacement of cement with GCFS on the 28-d and 56-d compressive strength of concrete. The results showed that temperature and % GCFS content had a statistically significant effect on the concrete performance. Based on Tukey’s honestly significant difference (HSD), the effect of GCFS was found to be statistically non-significant for 4% and 6% GCFS content at 28-d; and 2% and 4% GCFS content at 56-d. The effect of temperature was also found to be statistically non-significant for 600oC and 800oC at 28-d; and 27oC and 600oC; 200oC and 400oC at 56-d

Experimental investigation and prediction of compresive strength of concrete using soft computing techniques with different additives

High Performance Concrete (HPC) is the latest development in concrete, But HPC not only\ud demands High cement consumption, which pushes the natural resources towards depletion,\ud but also increases C02 emission on a higher extent. In the recent year’s use of Supplementary\ud Cementitious Materials (SCMs) is increased due to environment concerns, conservation of\ud resource & economy because most of them are generally Industrial waste products such as fly\ud ash, GGBS & micro silica. One of the costliest constituent of HPC is ultrafine material such\ud as micro silica, alccofine. In recent years with the advancement in technology ultrafine fly ash\ud is now being produced which is cheaper ultrafine material but, with less literature available on\ud it. In available literature on Ternary blend concrete the level of replacement was restricted up\ud to 30%-35%.\ud In this Experimental Investigation an attempt was made to investigate compressive strength\ud (100MPa) of concrete by replacing Cement on 40%, 45%, 50%, by incorporating P100 fly ash\ud as an ultrafine material and GGBS.\ud Each replacement was further divided into three sub parts (40%F.A-60%GGBS), (45%F.A-\ud 55%GGBS), (50%F.A-50%GGBS). Among which 40% replacement of cement (50%F.a-\ud 50%GGBS) gave maximum strength. Nominal mix was prepared with only OPC with w/c of\ud 0.24.and all other ternary mixes was made on w/c of 0.2 to have an edge when compared with\ud strength of nominal mix.\ud Nowadays, soft computing techniques are used to predict the properties of concrete and hence\ud reduce the experimental work. Thus, a neural network also known as a parallel distributed\ud processing network, is used as computing paradigm that is loosely modeled after structures of\ud the brain. It consists of interconnected processing elements called nodes or neurons that work\ud together to produce an output function.\ud This experimental investigation presents the application of Multiple Linear Regression (MLR)\ud and Artificial Neural Network (ANN) techniques for developing the model to predict the\ud compressive strength of the concrete with SCMs. For this purpose, a systematic laboratory\ud investigation was carried out. The compressive strength was evaluated on various mixes for 3\ud days, 7days, 14 days and 28 days of curing period. The data generated in the lab was used for development of the MLR and ANN model. The data used in the models are arranged in the\ud format of four input parameters that cover the contents of OPC, FA, GGBS and w/c ratio\ud respectively and one dependent variable as compressive strength of concrete for both MLR\ud and ANN. Networks are trained and tested for various combinations input and output data\ud sets.\ud Keywords: High Performance Concrete (HPC), Supplementary Cementitious Materials\ud (SCMs), Fly Ash (FA), Ground Granulated Blast Furnace Slag (GGBS), Artificial Neural\ud Network (ANN), Multi Linear Regression (MLR)

Prediction of Mortar Compressive Strengths for Different Cement Grades in the Vicinity of Sodium Chloride Using ANN

AbstractThe compressive strength values of cement mortar usually affect by sodium chloride quantities, chemical admixtures and cement grades so that an artificial neural network model was performed to predict the compressive strength of mortar value for different cement grades and sodium chloride (NaCl) percent. A three layer feed forward artificial neural network (ANN) model having four input neurons such as cement grades, various water to cement ratio, sodium chloride solution content, one output neuron and five hidden neurons was developed to predict of mortar each compressive strength.To this aim, twelve different mixes under three sodium chloride solution of 0%, 5% and 10% submerged after 60 days has been adopted to measure compressive strength.Artificial neural network (ANN) analysis indicated that by using ANN as non-linear statistical data modeling tool, a strong correlation between the sodium chloride percent of cement mortar and compressive strength can be established. Moreover modeling tools has great influence on the different cement grade such as 42.5 and 32.5 MPa

Optimization of Blast Furnace Parameters using Artificial Neural Network

Inside the blast furnace (BF) the process is very complicated and very tough to model mathematically. Blast furnace is the heart of the steel industry as it produces molten pig iron which is the raw material for steel making. It is very important to minimise the operational cost, reduce fuel consumption, and optimise the overall efficiency of the blast furnace and also improve the productivity of the blast furnace. Therefore a multi input multi output (MIMO) artificial neural network (ANN) model has been developed to predict the parameters namely raceway adiabatic flame temperature (RAFT), shaft temperature and uptake temperature. The input parameters in the ANN model are oxygen enrichment, blast volume, blast pressure, top gas pressure, hot blast temperature (HBT), steam injection rate, stove cooler inlet temperature, & stove cooler outlet temperature. For the optimisation of the predictive output back propagation ANN model has been introduced. In this present work, Artificial Neural Network (ANN) has been used to predict and optimise the output parameters. All the input data were collected from Rourkela steel plant (RSP) of blast number IV during the one month of operation

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

This paper investigates the capability of utilizing Multivariate Adaptive Regression Splines (MARS) and Gene Expression Programing (GEP) methods to estimate the compressive strength of self-compacting concrete (SCC) incorporating Silica Fume (SF) as a supplementary cementitious materials. In this regards, a large experimental test database was assembled from several published literature, and it was applied to train and test the two models proposed in this paper using the mentioned artificial intelligence techniques. The data used in the proposed models are arranged in a format of seven input parameters including water, cement, fine aggregate, specimen age, coarse aggregate, silica fume, super-plasticizer and one output. To indicate the usefulness of the proposed techniques statistical criteria are checked out. The results testing datasets are compared to experimental results and their comparisons demonstrate that the MARS (R2=0.98 and RMSE= 3.659) and GEP (R2=0.83 and RMSE= 10.362) approaches have a strong potential to predict compressive strength of SCC incorporating silica fume with great precision. Performed sensitivity analysis to assign effective parameters on compressive strength indicates that age of specimen is the most effective variable in the mixture

Gaussian Regression Process for Prediction of Compressive Strength of Thermally Activated Geopolymer Mortars

The primary objective of this research is the development of a prediction model of the compressive strength of geopolymer mortars made with fly ash and granular slag which hardened in different curing conditions. Data for the numerical analysis were obtained by experimental research; for this purpose 45 series of geopolymer mortars were made, 9 of which were cured in ambient conditions at a temperature of 22 °С, and the remaining were exposed to thermal activation for a duration of 24 h at the temperatures of 65 °С, 75 °С, 85 °С and 95 °С. Using machine learning, a Gaussian regression method was developed in which the curing temperature and the percentage mass content of fly ash and granular slag were used as input parameters, and the compressive strength as the output. Based on the results of the developed model, it can be concluded that the Gaussian regression process can be used as a reliable regression method for predicting the compressive strength of geopolymer mortars based on fly ash and granular slag