Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

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Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Materials ◽

10.3390/ma14040794 ◽

2021 ◽

Vol 14 (4) ◽

pp. 794 ◽

Cited By ~ 1

Author(s):

Ayaz Ahmad ◽

Furqan Farooq ◽

Pawel Niewiadomski ◽

Krzysztof Ostrowski ◽

Arslan Akbar ◽

...

Keyword(s):

Fly Ash ◽

Decision Tree ◽

Mean Square Error ◽

Gene Expression Programming ◽

Model Performance ◽

Absolute Error ◽

Machine Learning Techniques ◽

Mean Square ◽

Learning Techniques ◽

Moderate Bias

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

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PEMODELAN PREDIKTIF KONSUMSI ENERGI BANGUNAN GEDUNG KOMERSIAL DENGAN ALGORITMA SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v14i2.882 ◽

2018 ◽

Vol 14 (2) ◽

pp. 225

Author(s):

Indriyanti Indriyanti ◽

Agus Subekti

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Absolute Error ◽

Absolute Error ◽

Support Vector ◽

Mean Square

Konsumsi energi bangunan yang semakin meningkat mendorong para peneliti untuk membangun sebuah model prediksi dengan menerapkan metode machine learning, namun masih belum diketahui model yang paling akurat. Model prediktif untuk konsumsi energi bangunan komersial penting untuk konservasi energi. Dengan menggunakan model yang tepat, kita dapat membuat desain bangunan yang lebih efisien dalam penggunaan energi. Dalam tulisan ini, kami mengusulkan model prediktif berdasarkan metode pembelajaran mesin untuk mendapatkan model terbaik dalam memprediksi total konsumsi energi. Algoritma yang digunakan yaitu SMOreg dan LibSVM dari kelas Support Vector Machine, kemudian untuk evaluasi model berdasarkan nilai Mean Absolute Error dan Root Mean Square Error. Dengan menggunakan dataset publik yang tersedia, kami mengembangkan model berdasarkan pada mesin vektor pendukung untuk regresi. Hasil pengujian kedua algoritma tersebut diketahui bahwa algoritma SMOreg memiliki akurasi lebih baik karena memiliki nilai MAE dan RMSE sebesar 4,70 dan 10,15, sedangkan untuk model LibSVM memiliki nilai MAE dan RMSE sebesar 9,37 dan 14,45. Kami mengusulkan metode berdasarkan algoritma SMOreg karena kinerjanya lebih baik.

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Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Materials ◽

10.3390/ma14195762 ◽

2021 ◽

Vol 14 (19) ◽

pp. 5762

Author(s):

Waqas Ahmad ◽

Ayaz Ahmad ◽

Krzysztof Adam Ostrowski ◽

Fahid Aslam ◽

Panuwat Joyklad ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Compressive Strength ◽

Mean Square Error ◽

Gene Expression Programming ◽

Cementitious Materials ◽

Supplementary Cementitious Materials ◽

Supervised Machine Learning ◽

Mean Square ◽

Compressive Strength Of Concrete

The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures.

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Cricket Match Analytics Using the Big Data Approach

Electronics ◽

10.3390/electronics10192350 ◽

2021 ◽

Vol 10 (19) ◽

pp. 2350

Author(s):

Mazhar Javed Awan ◽

Syed Arbaz Haider Gilani ◽

Hamza Ramzan ◽

Haitham Nobanee ◽

Awais Yasin ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Linear Regression ◽

Mean Square Error ◽

Selection Process ◽

Big Data Analytics ◽

Absolute Error ◽

Mean Square ◽

Data Framework ◽

The Individual

Cricket is one of the most liked, played, encouraged, and exciting sports in today’s time that requires a proper advancement with machine learning and artificial intelligence (AI) to attain more accuracy. With the increasing number of matches with time, the data related to cricket matches and the individual player are increasing rapidly. Moreover, the need of using big data analytics and the opportunities of utilizing this big data effectively in many beneficial ways are also increasing, such as the selection process of players in the team, predicting the winner of the match, and many more future predictions using some machine learning models or big data techniques. We applied the machine learning linear regression model to predict the team scores without big data and the big data framework Spark ML. The experimental results are measured through accuracy, the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), respectively 95%, 30.2, 1350.34, and 28.2 after applying linear regression in Spark ML. Furthermore, our approach can be applied to other sports.

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Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽

10.3390/vibration4020022 ◽

2021 ◽

Vol 4 (2) ◽

pp. 341-356

Author(s):

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Mean Absolute Error ◽

Absolute Error ◽

Machine Learning Techniques ◽

Rolling Stock ◽

Raw Data ◽

Learning Techniques ◽

Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

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Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction

Intelligent Decision Technologies ◽

10.3233/idt-200093 ◽

2021 ◽

pp. 1-9

Author(s):

Rajashree Dash ◽

Anuradha Routray ◽

Rasmita Dash ◽

Rasmita Rautray

Keyword(s):

Mean Square Error ◽

Optimization Technique ◽

Absolute Error ◽

Time Frame ◽

Gold Price ◽

Future Price ◽

Mean Square ◽

Learning Speed ◽

Predictor Model ◽

Hidden Layer

Predicting future price of Gold has always been an intriguing field of investigation for researchers as well as investors who desire to invest in present and gain profit in the future. Since ancient time, Gold is being arbitrated as a leading asset in monetary business. As the worth of gold changes within confined boundaries, reducing the effect of inflation, so it is a beneficial property favoured by many stakeholders. Hence, there is always an urge of a more authenticate model for forecasting the gold price based upon the changes in it in a previous time frame. This study focuses on designing an efficient predictor model using a Pi-Sigma Neural Network (PSNN) for forecasting future gold. The underlying motivation of using PSNN is its quick learning and easy implementation compared to other neural networks. The fixed unit weights used in between hidden and output layer of PSNN helps it in achieving faster learning speed compared to other similar types of networks. But estimating the unknown weights used in between the input and hidden layer is still a major challenge in its design phase. As final outcome of the network is highly influenced by its weight, so a novel Crow Search based nature inspired optimization algorithm (CSA) is proposed to estimate these adjustable weights of the network. The proposed model is also compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of PSNN. The model is validated over two historical datasets such as Gold/INR and Gold/AED by considering three statistical errors such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Empirical observations clearly show that, the developed CSA-PSNN predictor model is providing better prediction results compared to PSO-PSNN and DE-PSNN model.

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Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

10.26434/chemrxiv.11973273.v4 ◽

2021 ◽

Author(s):

FNU SRINIDHI

Keyword(s):

Carbon Dioxide ◽

Supercritical Carbon Dioxide ◽

Mean Square Error ◽

Supercritical Co2 ◽

Mean Absolute Error ◽

Absolute Error ◽

Mean Square ◽

Relational Analysis ◽

Grey Relational ◽

Supercritical Carbon

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

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Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽

10.3390/ma15020647 ◽

2022 ◽

Vol 15 (2) ◽

pp. 647

Author(s):

Meijun Shang ◽

Hejun Li ◽

Ayaz Ahmad ◽

Waqas Ahmad ◽

Krzysztof Adam Ostrowski ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Mean Square Error ◽

Coarse Aggregate ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Environmental Damage ◽

Fine Aggregate ◽

Mean Square ◽

The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

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Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3488378 ◽

2022 ◽

Vol 16 (4) ◽

pp. 1-22

Author(s):

Chang Liu ◽

Jie Yan ◽

Feiyue Guo ◽

Min Guo

Keyword(s):

Machine Learning ◽

Stock Market ◽

Mean Square Error ◽

Short Term Memory ◽

The State ◽

Machine Learning Algorithms ◽

Future Market ◽

Mean Square ◽

Market Data ◽

Market Trends

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market trends have material impacts on market trend forecasting (2) the length of past market data should be dynamically adjusted according to the market status and (3) the transition of market statutes should be considered when forecasting market trends. In this study, we proposed an innovative ML method to forecast China's stock market trends by addressing the three issues above. Specifically, sentimental factors (see Appendix [1] for full trans) were first collected to measure investors’ emotions and attitudes. Then, a non-stationary Markov chain (NMC) model was used to capture dynamic transitions of market statutes. We choose the state-of-the-art (SOTA) method, namely, Bidirectional Encoder Representations from Transformers ( BERT ), to predict the state of the market at time t , and a long short-term memory ( LSTM ) model was used to estimate the varying length of past market data in market trend prediction, where the input of LSTM (the state of the market at time t ) was the output of BERT and probabilities for opening and closing of the gates in the LSTM model were based on outputs of the NMC model. Finally, the optimum parameters of the proposed algorithm were calculated using a reinforced learning-based deep Q-Network. Compared to existing forecasting methods, the proposed algorithm achieves better results with a forecasting accuracy of 61.77%, annualized return of 29.25%, and maximum losses of −8.29%. Furthermore, the proposed model achieved the lowest forecasting error: mean square error (0.095), root mean square error (0.0739), mean absolute error (0.104), and mean absolute percent error (15.1%). As a result, the proposed market forecasting model can help investors obtain more accurate market forecast information.

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Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India

Sensors ◽

10.3390/s20051313 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1313 ◽

Cited By ~ 15

Author(s):

Sunil Saha ◽

Jagabandhu Roy ◽

Alireza Arabameri ◽

Thomas Blaschke ◽

Dieu Tien Bui

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Absolute Error ◽

Gully Erosion ◽

Machine Learning Techniques ◽

Weight Of Evidence ◽

Validation Dataset ◽

Boosted Regression Tree ◽

Area Index ◽

Statistical Measures

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.

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