scholarly journals Machine Learning-Based Prediction of the Seismic Bearing Capacity of a Shallow Strip Footing over a Void in Heterogeneous Soils

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 288
Author(s):  
Mohammad Sadegh Es-haghi ◽  
Mohsen Abbaspour ◽  
Hamidreza Abbasianjahromi ◽  
Stefano Mariani
Keyword(s):  
Machine Learning ◽  
Bearing Capacity ◽  
Multilayer Perceptron ◽  
Strip Footing ◽  
Upper And Lower Bounds ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Group Method ◽  
Inference System ◽  
Seismic Bearing Capacity

The seismic bearing capacity of a shallow strip footing above a void displays a complex dependence on several characteristics, linked to geometric problems and to the soil properties. Hence, setting analytical models to estimate such bearing capacity is extremely challenging. In this work, machine learning (ML) techniques have been employed to predict the seismic bearing capacity of a shallow strip footing located over a single unsupported rectangular void in heterogeneous soil. A dataset consisting of 38,000 finite element limit analysis simulations has been created, and the mean value between the upper and lower bounds of the bearing capacity has been computed at the varying undrained shear strength and internal friction angle of the soil, horizontal earthquake accelerations, and position, shape, and size of the void. Three machine learning techniques have been adopted to learn the link between the aforementioned parameters and the bearing capacity: multilayer perceptron neural networks; a group method of data handling; and a combined adaptive-network-based fuzzy inference system and particle swarm optimization. The performances of these ML techniques have been compared with each other, in terms of the following statistical performance indices: coefficient of determination (); root mean square error (); mean absolute percentage error; scatter index; and standard bias. Results have shown that all the ML techniques perform well, though the multilayer perceptron has a slightly superior accuracy featuring noteworthy results (0.9955 and 0.0158).

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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

2020 ◽  
Vol 9 (3) ◽  
pp. 674 ◽  
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.

Seismic bearing capacity of a strip footing on rock slopes

2009 ◽  
Vol 46 (8) ◽  
pp. 943-954 ◽  
Author(s):  
Xiao-Li Yang
Keyword(s):  
Bearing Capacity ◽  
Failure Criterion ◽  
Strip Footing ◽  
Rock Slopes ◽  
Coulomb Failure ◽  
Coulomb Failure Criterion ◽  
Seismic Bearing Capacity ◽  
Earthquake Effects ◽  
Almost All ◽  
Bearing Capacity Factor

Most of the seismic calculations currently used for the evaluation of seismic bearing capacity are formulated in terms of a linear Mohr–Coulomb failure criterion. However, experimental evidence shows that a nonlinear failure criterion is able to represent fairly well the failure of almost all types of rocks. In this paper, a nonlinear Hoek–Brown failure criterion is used to estimate the seismic bearing capacity factor of a strip footing on rock slopes in a limit analysis framework. Quasi-static representation of earthquake effects using a seismic coefficient is adopted for the seismic bearing capacity calculations. A linear Mohr–Coulomb failure criterion, tangent to the nonlinear Hoek–Brown failure criterion, is used to derive the objective function that is to be minimized. Upper-bound solutions are obtained by optimization. For static problems, bearing capacity factors related to uniaxial compressive strength, Nσ, are compared. For seismic problems, Nσ factors for different ground inclinations are presented for practical use in rock engineering.

Application of Neural Networks To Simulate the Growth Profile of Lactic Acid Bacteria in Green Olive Fermentation

2007 ◽  
Vol 70 (8) ◽  
pp. 1909-1916 ◽  
Author(s):  
EFSTATHIOS Z. PANAGOU ◽  
CHRYSOULA C. TASSOU ◽  
ELEFTHERIOS K. A. SARAVANOS ◽  
GEORGE-JOHN E. NYCHAS
Keyword(s):  
Experimental Data ◽  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
Multilayer Perceptron ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Data Set ◽  
Growth Profile ◽  
Multilayer Perceptron Network ◽  
Green Olive

The growth profile of five strains of lactic acid bacteria (Lactobacillus plantarum ACA-DC 287, L. plantarum ACA-DC 146, Lactobacillus paracasei ACA-DC 4037, Lactobacillus sakei LQC 1378, and Leuconostoc mesenteroides LQC 1398) was investigated in controlled fermentation of cv. Conservolea green olives with a multilayer perceptron network, a combined logistic-Fermi function, and a two-term Gompertz function. Neural network training was based on the steepest-descent gradient learning algorithm. Model performance was compared with the experimental data with five statistical indices, namely coefficient of determination (R2), root mean square error (RMSE), mean relative percentage error (MRPE), mean absolute percentage error (MAPE), and standard error of prediction (SEP). The experimental data set consisted of 125 counts (CFU per milliliter) of lactic acid bacteria during the green olive fermentation process for up to 38 days (5 strains × 25 sampling days). For model development, a standard methodology was followed, dividing the data set into training (120 data) and validation (25 data) subsets. Our results demonstrated that the developed network was able to model the growth and survival profile of all the strains of lactic acid bacteria during fermentation equally well with the statistical models. The performance indices for the training subset of the multilayer perceptron network were R2 = 0.987, RMSE = 0.097, MRPE = 0.069, MAPE = 0.933, and SEP = 1.316. The relevant mean values for the logistic-Fermi and two-term Gompertz functions were R2 = 0.981 and 0.989, RMSE = 0.109 and 0.083, MRPE = 0.026 and 0.030, MAPE = 1.430 and 1.076, and SEP = 1.490 and 1.127, respectively. For the validation subset, the network also gave good predictions (R2 = 0.968, RMSE = 0.149, MRPE = 0.100, MAPE = 1.411, and SEP = 2.009).

Pseudo-Dynamic Bearing Capacity of Shallow Strip Footing Resting on c-Φ Soil Considering Composite Failure Surface

2015 ◽  
Vol 6 (2) ◽  
pp. 12-34 ◽  
Author(s):  
Arijit Saha ◽  
Sima Ghosh
Keyword(s):  
Bearing Capacity ◽  
Failure Mechanism ◽  
Limit Equilibrium ◽  
Failure Surface ◽  
Friction Angle ◽  
Strip Footing ◽  
Unit Weight ◽  
Composite Failure ◽  
Seismic Accelerations ◽  
Seismic Bearing Capacity

The evaluation of bearing capacity of shallow strip footing under seismic loading condition is an important phenomenon. This paper presents a pseudo-dynamic approach to evaluate the seismic bearing capacity of shallow strip footing resting on c-F soil using limit equilibrium method considering the composite failure mechanism. A single seismic bearing capacity coefficient (N?e) presents here for the simultaneous resistance of unit weight, surcharge and cohesion, which is more practical to simulate the failure mechanism. The effect of soil friction angle(F), soil cohesion(c), shear wave and primary wave velocity(Vs, Vp) and horizontal and vertical seismic accelerations(kh, kv) are taken into account to evaluate the seismic bearing capacity of foundation. The results obtained from the present analysis are presented in both tabular and graphical non-dimensional form. Results are thoroughly compared with the existing values in the literature and the significance of the present methodology for designing the shallow strip footing is discussed.

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