Influential parameters on submerged discharge capacity of converging ogee spillways based on experimental study and machine learning-based modeling

2019 ◽  
Vol 21 (3) ◽  
pp. 474-492 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Ali Foroudi ◽  
Mojtaba Saneie
Keyword(s):  
Machine Learning ◽  
Discharge Capacity ◽  
Gene Expression Programming ◽  
Theoretical Concept ◽  
Visual Observation ◽  
Experimental Models ◽  
Machine Learning Techniques ◽  
Construction Operations ◽  
Effective Role ◽  
Submerged Discharge

Abstract Ogee spillways with converging training walls are applied to lower the hazard of accidental flooding in locations with limited construction operations due to their unique structure. Hence, this type of structure is proposed as an emergency spillway. The present study aimed at experimental and machine learning-based modeling of the submerged discharge capacity of the converging ogee spillway. Two experimental models of Germi-Chay dam spillway were utilized: one model having a curve axis which was made in 1:50 scale and the other with a straight axis in 1:75 scale. Using visual observation, it was found that the total upstream head, the submergence degree, the ogee-crest geometries and the convergence angle of training walls are the crucial factors which alter the submerged discharge capacity of the converging ogee spillway. Furthermore, two machine-learning techniques (e.g. artificial neural networks and gene expression programming) were applied for modeling the submerged discharge capacity applying experimental data. These models were compared with four well-known traditional relationships with respect to their basic theoretical concept. The obtained results indicated that the length ratio () had the most effective role in estimating the submerged discharge capacity.

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

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2297
Author(s):  
Ayaz Ahmad ◽  
Furqan Farooq ◽  
Krzysztof Adam Ostrowski ◽  
Klaudia Śliwa-Wieczorek ◽  
Slawomir Czarnecki
Keyword(s):  
Machine Learning ◽  
Mean Square Error ◽  
Gene Expression Programming ◽  
Chloride Concentration ◽  
Absolute Error ◽  
Chloride Ions ◽  
Machine Learning Techniques ◽  
Extensive Literature ◽  
Mean Square ◽  
Chloride Concentrations

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.

scholarly journals Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 58
Author(s):  
Mohsin Ali Khan ◽  
Furqan Farooq ◽  
Mohammad Faisal Javed ◽  
Adeel Zafar ◽  
Krzysztof Adam Ostrowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fly Ash ◽  
Gene Expression Programming ◽  
External Validation ◽  
Experimental Tests ◽  
Machine Learning Techniques ◽  
Explanatory Variables ◽  
Fly Ash Concrete ◽  
Response Parameter ◽  
Skilled Personnel

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

scholarly journals Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation

2019 ◽  
Author(s):  
Ali Rashid Niaghi ◽  
Oveis Hassanijalilian ◽  
Jalal Shiri
Keyword(s):  
Machine Learning ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Gene Expression Programming ◽  
Machine Learning Techniques ◽  
Limiting Factors ◽  
Support Vector ◽  
Daily Maximum ◽  
Climate Data ◽  
Input Variables

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

A Digital Twin for Real-Time Drilling Hydraulics Simulation Using a Hybrid Approach of Physics and Machine Learning

2021 ◽  
Author(s):  
Prasanna Amur Varadarajan ◽  
Ghislain Roguin ◽  
Nick Abolins ◽  
Maurice Ringer
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Event Detection ◽  
Hybrid Approach ◽  
Time Series Forecasting ◽  
Machine Learning Techniques ◽  
Construction Operations ◽  
Model Comparisons ◽  
Learning Techniques

Abstract Abnormal hydraulic event detection is essential for offshore well construction operations. These operations require model comparisons and real-time measurements. For this task, physics-based models, which need frequent manual calibration do not accurately capture all the hydraulic trends. The paper presents a method to overcome existing limitations by combining physics-based models with machine learning techniques, which are suited for time series forecasting. This method ensures accurate and reliable predictions during the forecasting period and helps remove the need for frequent manual calibration of the hydraulic input parameters.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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