scholarly journals Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection

2021 ◽  
Vol 5 (10) ◽  
pp. 271
Author(s):  
Priyanka Gupta ◽  
Nakul Gupta ◽  
Kuldeep K. Saxena ◽  
Sudhir Goyal
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Fly Ash ◽  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Curing Temperature ◽  
Coefficient Of Determination ◽  
Split Tensile Strength ◽  
Calcined Clay

Geopolymer is an eco-friendly material used in civil engineering works. For geopolymer concrete (GPC) preparation, waste fly ash (FA) and calcined clay (CC) together were used with percentage variation from 5, 10, and 15. In the mix design for geopolymers, there is no systematic methodology developed. In this study, the random forest regression method was used to forecast compressive strength and split tensile strength. The input content involved were caustic soda with 12 M, 14 M, and 16 M; sodium silicate; coarse aggregate passing 20 mm and 10 mm sieve; crushed stone dust; superplasticizer; curing temperature; curing time; added water; and retention time. The standard age of 28 days was used, and a total of 35 samples with a target-specified compressive strength of 30 MPa were prepared. In all, 20% of total data were trained, and 80% of data testing was performed. Efficacy in terms of mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and MSE (mean squared error) is suggested in the model. The results demonstrated that the RFR model is likely to predict GPC compressive strength (MAE = 1.85 MPa, MSE = 0.05 MPa, RMSE = 2.61 MPa, and R2 = 0.93) and split tensile strength (MAE = 0.20 MPa, MSE = 6.83 MPa, RMSE = 0.24 MPa, and R2 = 0.90) during training.

scholarly journals Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253006
Author(s):  
Hemn Unis Ahmed ◽  
Ahmed Salih Mohammed ◽  
Azad A. Mohammed ◽  
Rabar H. Faraj
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Sodium Silicate ◽  
Mean Squared Error ◽  
Curing Temperature ◽  
Coefficient Of Determination ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Palm Oil Fuel Ash ◽  
Binder Ratio

Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.

scholarly journals Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1072 ◽  
Author(s):  
Dong Van Dao ◽  
Hai-Bang Ly ◽  
Huong-Lan Thi Vu ◽  
Tien-Thinh Le ◽  
Binh Thai Pham
Keyword(s):  
Compressive Strength ◽  
Civil Engineering ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Dry Density ◽  
Statistical Measures ◽  
Foamed Concrete

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

scholarly journals Effect of Carboxylic Acid (Benzene Crystallable) in Cement Concrete and Geopolymer Concrete

2020 ◽  
Vol 9 (7) ◽  
pp. 472-475
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Fly Ash ◽  
Carboxylic Acid ◽  
Geopolymer Concrete ◽  
Cement Concrete ◽  
Split Tensile Strength ◽  
Sealing Capacity ◽  
Time Period

The present study appraises the recitals of carboxylic acid- based admixture to increase concrete water tightness and self-sealing capacity of the cement and geopolymer concrete. Outcomes of the previous studies in particular, adding 1% by cement mass of the carboxylic polymer reasons for reduction in the water dispersion under pressure of 7-day wet cured concrete by 50% associated to that of the conforming reference concrete. At 7 days, M4 mix compressive strength is about 43.5% less than M3 mix. The compressive strength of M4 increases and is about 37.6% less than M3 mix at 28 days of curing. At 7 days, M4 mix split tensile strength is about 17.5% less than M3 mix (cement concrete with 0.45 w/c ratio). The split tensile strength of M4 declines and is about 42.3% less than M3 mix at 28 days of curing. The strength of the geopolymer concrete tends to increase as the time period increases due to the presence of fly ash in it. So it is expected that geopolymer concrete will give more strength than cement concrete in long term with the presence of carboxylic acid

scholarly journals Evaluation of Random Forest for short-term daily streamflow forecast in rainfall and snowmelt driven watersheds

2020 ◽  
Author(s):  
Leo T. Pham ◽  
Lifeng Luo ◽  
Andrew O. Finley
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Climatic Conditions ◽  
Coefficient Of Determination ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Streamflow Forecast ◽  
Daily Streamflow ◽  
Rf Performance

Abstract. In the past decades, data-driven Machine Learning (ML) models have emerged as promising tools for short-term streamflow forecasts. Among other qualities, the popularity of ML for such applications is due to the methods' competitive performance compared with alternative approaches, ease of application, and relative lack of strict distributional assumptions. Despite the encouraging results, most applications of ML for streamflow forecast have been limited to watersheds where rainfall is the major source of runoff. In this study, we evaluate the potential of Random Forest (RF), a popular ML method, to make streamflow forecast at 1-day lead time at 86 watersheds in the Pacific Northwest. These watersheds span climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes: rainfall-dominated, transisent, and snowmelt-dominated based on the timing of center of annual flow volume. RF performance is benchmarked against Naive and multiple linear regression (MLR) models, and evaluated using four metrics Coefficient of determination, Root mean squared error, Mean absolute error, and Kling-Gupta efficiency. Model evaluation metrics suggest RF performs better in snowmelt-driven watersheds. Largest improvement in forecasts, compared to benchmark models, are found among rainfall-driven watersheds. We obtain Kling–Gupta Efficiency (KGE) scores in the range of 0.62–0.99. RF performance deteriorates with increase in catchment slope and increase in soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.

scholarly journals Forecasting New Student Candidates Using the Random Forest Method

2020 ◽  
Vol 11 (1) ◽  
pp. 44
Author(s):  
Rahmat Robi Waliyansyah ◽  
Nugroho Dwi Saputro
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
College Education ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Squared Error ◽  
Random Forest Method ◽  
Study Programs ◽  
New Students ◽  
New Student

College education institutions regularly hold new student admissions activities, and the number of new students can increase and can also decrease. University of PGRI Semarang (UPGRIS) on the development of new student admissions for the 2014/2015 academic year up to 2018/2019 with so many admissions selection stages. To meet the minimum comparison requirements between the number of students with the development of human resources, facilities, and infrastructure, it is necessary to predict how much the number of students increases each year. To make a prediction system or forecasting, the number of prospective new students required a good forecasting method and sufficiently precise calculations to predict the number of prospective students who register. In this study, the method to be taken is the Random Forest method. For the evaluation of forecasting models used Random Sampling and Cross-validation. The parameter used is Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results of this study obtained the five highest and lowest study programs in the admission of new students. Therefore, UPGRIS will make a new strategy for the five lowest study programs so that the desired number of new students is achieved

scholarly journals Rice Husk Ash and Fly Ash Effects on the Mechanical Properties of Concrete

2020 ◽  
Vol 10 (2) ◽  
pp. 5402-5405 ◽  
Author(s):  
N. Bheel ◽  
M. A. Jokhio ◽  
J. A. Abbasi ◽  
H. B. Lashari ◽  
M. I. Qureshi ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Energy Consumption ◽  
Fly Ash ◽  
Rice Husk ◽  
Carbon Dioxide Emissions ◽  
Rice Husk Ash ◽  
Split Tensile Strength ◽  
Cement Production ◽  
Indirect Tensile

Cement production involves high amounts of energy consumption and carbon dioxide emissions. Pakistan is facing a serious energy crisis and cement’s cost is increasing. In addition, landfilling of potential concrete components can lead to environmental degradation. The use of waste as cement replacement not only reduces cement production cost by reducing energy consumption, but it is also environmentally friendly. The purpose of this study is to analyze the characteristics of concrete by partially replacing cement with Rice Husk Ash (RHA) and Fly Ash (FA). This study is mainly focused on the performance of concrete conducting a slump test, and investigating indirect tensile and compressive strength. Cement was replaced with RHA and FA by 5% (2.5% RHA + 2.5% FA), 10% (5% RHA + 5% FA), 15% (7.5% RHA + 7.5% FA) and 20% (10% RHA+10% FA) by weight. Ninety concrete samples were cast with mix proportions of 1:2:4 and 0.55 water/cement ratio. Cube and cylindrical samples were used for measuring compressive and split tensile strength respectively, after 7 and 28 days. The results showed that after 28 days, the 5% RHA+5% FA sample’s compressive strength was enhanced by 16.14% and its indirect tensile strength was improved by 15.20% compared to the conventional sample. Moreover, the sample’s slump value dropped as the content of RHA and FA increased.

scholarly journals Mechanical properties of concrete composed of sintered fly ash lightweight aggregate

2018 ◽  
Vol 195 ◽  
pp. 01008
Author(s):  
Puput Risdanareni ◽  
Januarti Jaya Ekaputri ◽  
Ike Maulidiyawati ◽  
Poppy Puspitasari
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Fly Ash ◽  
Coarse Aggregate ◽  
Lightweight Aggregate ◽  
Effective Solution ◽  
Binding Agent ◽  
Split Tensile Strength ◽  
Bonded Method

This paper investigates the effect of sintered fly ash lightweight aggregate as coarse aggregate substitution on the mechanical properties of concrete. The lightweight aggregate (LWA) was produced using the cold bonded method and then sintered at a temperature of 900°C. An alkaliactivated system was applied as a binding agent of the LWA. Fly ash was used as precursor while sodium hydroxide and sodium silicate were employed as alkali activators. Three variations of the LWA dosage were performed, which were 0%, 50%, and 100 % of the volume of coarse aggregate in the concrete mixture. The mechanical properties of the concrete investigated in this research are the compressive strength and split tensile strength. The result showed that the mechanical properties of the concrete slightly decrease along with the increased dosage of the LWA in the mixture. However, employing sintered fly ash the LWA is proven as an effective solution in reducing the concrete density without sacrificing its strength.

scholarly journals Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-17 ◽  
Author(s):  
Mohsin Ali Khan ◽  
Shazim Ali Memon ◽  
Furqan Farooq ◽  
Muhammad Faisal Javed ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Fly Ash ◽  
Random Forest ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Soft Computing Techniques ◽  
Water Ratio

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T , curing time t , age of the specimen A , the molarity of NaOH solution M , percent SiO2 solids to water ratio %   S / W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate (   %   A G ), fine aggregate to the total aggregate ratio F / A G , sodium oxide (Na2O) to water ratio N / W in Na2SiO3 solution, alkali or activator to the FA ratio A L / F A , Na2SiO3 to NaOH ratio N s / N o , percent plasticizer ( %   P ), and extra water added as percent FA E W % . RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

Statistical Models to Predict Tensile Strength from Unconfined Compressive Strength: Case Study from Southern Iraq

2021 ◽  
Author(s):  
Husam Hasan Alkinani ◽  
Abo Taleb Tuama Al-Hameedi ◽  
Shari Dunn-Norman ◽  
Mustafa Adil Al-Alwani
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Least Squares ◽  
Unconfined Compressive Strength ◽  
Mean Squared Error ◽  
Time Factors ◽  
Absolute Error ◽  
Tensile Failure ◽  
Least Squares Regression ◽  
Southern Iraq

Abstract Tensile strength (To) is an important parameter for creating geomechanical models, especially when tensile failure is the failure of interest. The most common way to estimate the tensile strength is by utilizing Brazilian tests. However, due to material limitation, cost, or time, To is sometimes assumed or estimated empirically. In this work, laboratory test data of To and Unconfined Compressive Strength (UCS) conducted for three zones in southern Iraq (Zubair sandstone, Zubair shale, and Nahr Umr shale) were utilized to create three regression models to estimate To from UCS. The reason for selecting UCS as the independent parameter is that static UCS, in most cases, has to be estimated from laboratory tests to create robust geomechanical models. In other words, UCS will be given the preference over Towhen there is the material limitation, cost, or time involved. The data of each zone were divided into training (80%) and testing (20%) to ensure the models can generalize for new data and avoid overfitting. Multiple least squares fits were tested, and linear least squares regression was selected since it provided the highest R2 and the lowest error. The models yielded training R2 of 0.983, 0.988, and 0.965 while the testing R2 were 0.978, 0.990, and 0.993 for Zubair sandstone, Zubair shale, and Nahr Umr shale, respectively. The errors were assessed using root mean squared error (RMSE) and mean absolute error (MAE), and they both have shown an acceptable margin of error for all three models. In short, the created three models showed the ability to estimate To from UCS when material limitation, cost, or time factors are involved or when executing a Brazilian test is not applicable. The proposed models can contribute to robust geomechanical models as well as minimizing cost, time, and material usage.

scholarly journals STUDI PEMANFAATAN ABU TERBANG LIMBAH HASIL PEMBAKARAN CAMPURAN CANGKANG DAN SERABUT KELAPA SAWIT SEBAGAI SUBSTITUSI SEMEN PADA PEMBUATAN BETON NORMAL DI LAHAN BASAH

INFO-TEKNIK ◽  
2020 ◽  
Vol 21 (2) ◽  
pp. 227
Author(s):  
Fauzi Rahman ◽  
Gawit Hidayat ◽  
Novita Bertiani
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Fly Ash ◽  
Oil Palm ◽  
Combustion Process ◽  
Strength Test ◽  
Strength Analysis ◽  
Normal Concrete ◽  
Split Tensile Strength ◽  
Tensile Strength Test

According to the Badan Pusat Statistik data in 2018, the total area of oil palm plantations in Indonesia currently reaches around 12.3 million hectares. Solid waste is the most waste, which is around 35-40% of the total Fresh Fruit Bunches (FFB) which is processed in the form of empty fruit bunches, fiber, fruit shells, and burnt ash. PT. Hasnur Citra Terpadu in Rantau, Tapin Regency, South Kalimantan is one of the Palm Oil Mill which in the combustion process of a boiler engine using oil palm shells and fibers is burned simultaneously. The result of the combustion process produces waste in the form of boiler crust ash which is fine textured (fly ash) and coarse textured (bottom ash). This study uses fly ash as a cement substitution for concrete mixtures. The making of mortar specimens was varied with fly ash with a percentage of 0%, 10%, 15%, 20% and 25% which will be tested for compressive strength at the age of 3 days, 7 days, 14 days, 21 days, and 28 days. Then the making of concrete specimens is planned with a quality of 25 MPa and the concrete compressive strength is tested at the age of 3 days, 7 days, 14 days, 28 days and 56 days and the split tensile strength test of the concrete at 28 days. Based on the results of the mortar compressive strength analysis, the optimum mixture of fly ash is 10% which is used for making concrete. The average compressive strength of normal concrete at 28 days is 26.33 MPa and the compressive strength of concrete with 10% fly ash (optimum concrete) is 26.14 MPa exceeding the design compressive strength of 25 MPa. Based on the results of the split tensile strength test of concrete at the age of 28 days, it was obtained 3,914 MPa for normal concrete and 3,466 MPa for optimum concrete.

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