Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature

Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R2), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R2 above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.

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Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17103520 ◽

2020 ◽

Vol 17 (10) ◽

pp. 3520 ◽

Cited By ~ 11

Author(s):

Mohammed A. A. Al-qaness ◽

Ahmed A. Ewees ◽

Hong Fan ◽

Laith Abualigah ◽

Mohamed Abd Elaziz

Keyword(s):

Mean Squared Error ◽

Fuzzy Inference ◽

Absolute Error ◽

Coefficient Of Determination ◽

Percentage Error ◽

Inference System ◽

Infected People ◽

Marine Predators ◽

Forecasting Performance ◽

The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

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How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

Epidemiology and Infection ◽

10.1017/s0950268821000091 ◽

2021 ◽

Vol 149 ◽

Author(s):

Junwen Tao ◽

Yue Ma ◽

Xuefei Zhuang ◽

Qiang Lv ◽

Yaqiong Liu ◽

...

Keyword(s):

Variable Selection ◽

Mean Squared Error ◽

Moving Average ◽

Dynamic Bayesian Networks ◽

Environmental Data ◽

Control Measures ◽

Coefficient Of Determination ◽

Percentage Error ◽

Analysis Strategy ◽

Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

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Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Journal of Clinical Medicine ◽

10.3390/jcm9030674 ◽

2020 ◽

Vol 9 (3) ◽

pp. 674 ◽

Cited By ~ 83

Author(s):

Mohammed A. A. Al-qaness ◽

Ahmed A. Ewees ◽

Hong Fan ◽

Mohamed Abd El Aziz

Keyword(s):

Relative Error ◽

Computing Time ◽

World Health ◽

Coefficient Of Determination ◽

Flower Pollination Algorithm ◽

Percentage Error ◽

Anfis Model ◽

Local Optima ◽

Inference System ◽

Proposed Model

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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The effects of using Micro-SiO2 with Ground Granulated Blast-Furnace Slag of Esfahan Steel Company in Concrete

Environment Conservation Journal ◽

10.36953/ecj.2015.se1660 ◽

2015 ◽

Vol 16 (SE) ◽

pp. 509-517

Author(s):

Fatemeh Sayyahi ◽

Hamid Shirzadi

Keyword(s):

Compressive Strength ◽

Blast Furnace ◽

Blast Furnace Slag ◽

Cement Content ◽

Harmful Effect ◽

Maximum Size ◽

Steel Company ◽

Granulated Blast Furnace Slag ◽

Compressive Strength Of Concrete ◽

Furnace Slag

In this study, the properties of concrete with different amounts of Ground Granulated Blast-Furnace Slag (GGBFS) has been studied. In another part, the test deals to assess the properties of concrete containing GGBFS with micro-SiO2. The results show that the slag has pozzolan properties and its use up to 20% in the concrete, has no harmful effect on concrete properties. The simultaneous use of micro-SiO2 with blast furnace slag have little effect, as well as micro-SiO2 covers the defects caused by the use of slag. The results indicate that the use of micro-SiO2 and slag has good effects on the strength of concrete up to a certain age, so that its compressive strength is increased. Water-cement ratio was 0.42 and 12.5 mm for maximum size of aggregate and cement content in concrete was 425 kg per cubic meter. Compressive strength of concrete samples was measured at ages 7, 28, 56 and 90-day and flexural and tensile strength and water absorption after 28-day and 90 days also was measured.

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Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Applied Sciences ◽

10.3390/app9183841 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3841 ◽

Cited By ~ 28

Author(s):

Ly ◽

Pham ◽

Dao ◽

Le ◽

...

Keyword(s):

Compressive Strength ◽

Mean Squared Error ◽

Fuzzy Inference ◽

Principal Component ◽

Absolute Error ◽

Anfis Model ◽

Natural Sand ◽

Inference System ◽

Computing Technique ◽

Manufactured Sand

Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

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

Materials ◽

10.3390/ma13051072 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1072 ◽

Cited By ~ 19

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.

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