scholarly journals A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Binh Thai Pham ◽  
Manh Duc Nguyen ◽  
Nadhir Al-Ansari ◽  
Quoc Anh Tran ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Permeability Coefficient ◽  
Construction Projects ◽  
Statistical Error ◽  
Clay Content ◽  
Cost Effective ◽  
Liquid Limit ◽  
Machine Learning Techniques ◽  
Design Stage ◽  
Accurate Estimation ◽  
Support Vector

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.

scholarly journals Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Bang Ly ◽  
Thuy-Anh Nguyen ◽  
Binh Thai Pham
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Soil Properties ◽  
Clay Content ◽  
Absolute Error ◽  
Experimental Methods ◽  
Liquid Limit ◽  
Support Vector ◽  
Data Set ◽  
Soil Cohesion

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

scholarly journals Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

2021 ◽  
Author(s):  
Wesam Salah Alaloul ◽  
Abdul Hannan Qureshi
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Industrial Revolution ◽  
Progress Monitoring ◽  
Concrete Block ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Material Classification ◽  
Artificial Neural Network Ann

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

scholarly journals RAPID IDENTIFICATION OF ABIOTIC STRESS (FROST) IN <i>IN-FILED</i> MAIZE CROP USING UAV REMOTE SENSING

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 467-471 ◽  
Author(s):  
J. Goswami ◽  
V. Sharma ◽  
B. U. Chaudhury ◽  
P. L. N. Raju
Keyword(s):  
Remote Sensing ◽  
Abiotic Stress ◽  
Computational Cost ◽  
Cost Effective ◽  
Rapid Identification ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Maize Crop ◽  
Multispectral Data

<p><strong>Abstract.</strong> Stress in the crop not only decreases the production but can also have devastating consequences for farmers whose life depends upon the healthy crops. In recent time (January 2018) a such abiotic stress event (hoar frost) was experienced at ICAR research complex experimental filed, Ri-Bhoi district of Meghalaya on standing Maize crop. Therefore, remote sensing (Multispectral UAV- Unmanned Aerial Vehicle) technology were used to detect the effect of frost on <i>in-filed</i> Maize crop. Two set of multispectral data (before frost and after frost) with four advanced machine learning techniques viz. Random Forest (RF), Random Committee (RC), Support Vector Machine (SVM) and Artificial Neural Network were employed for detection of stress free crop and stressed crop due to frost. Results revealed that all the four methods of classification could able to identify / detect stress-free vs. stressed crops at satisfactory level. However, among the classifiers RF achieved relatively higher overall accuracy (OA&amp;thinsp;=&amp;thinsp;86.47%) with Kappa Indexanalysis (KIA&amp;thinsp;=&amp;thinsp;0.80) and found very cost effective in context of computational cost (time complexity&amp;thinsp;=&amp;thinsp;0.08 Seconds) to train the model. In addition, we have also recorded the area of each classes and found that after frost stress-free area (36.01% of all over filed) is decreased by 11% in comparison of before frost (25.036% of all over filed). Based on the results we can suggest that the RF ensemble classification method can be used for further other crop classification in order to estimate the yield, detect the condition, monitoring the health etc.</p>

scholarly journals Evaluation of Ground Displacements Caused by Installing Jet Grouted Columns Using Machine Learning Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhi-Feng Wang ◽  
Xing-Bin Peng ◽  
Yong Liu ◽  
Wen-Chieh Cheng ◽  
Ya-Qiong Wang ◽  
...  
Keyword(s):  
Design Stage ◽  
Accurate Estimation ◽  
Support Vector ◽  
Target Point ◽  
Ground Displacement ◽  
Machine Learning Methods ◽  
Surrounding Environment ◽  
Jet Grouting ◽  
Pressurized Fluids ◽  
Ground Displacements

During the jet grouting process, large volumes of high pressurized fluids injected into the soils will cause significant ground displacements, which may bring harmful impacts on surrounding environment. Therefore, it is essential to provide an accurate estimation of the ground displacement in the design stage. Based on multiple nonlinear regression (MNLR) and support vector regression (SVR), the prediction approaches are established, respectively. The column radius (Rc), Young’s modulus (E), and distance from column center to target point (LOA) are selected as the input parameters, while the displacement of target point A at the radial direction (δA) is taken as the output parameter. Comparisons results on the prediction performance of ground displacements indicate that the MNLR-based approach has a better prediction effect. The design charts of the MNLR-based approach for predicting the ground displacement are created, which will be helpful for the practicing engineers to get a quick estimation.

scholarly journals Predictive modeling of turning operations under different cooling/lubricating conditions for sustainable manufacturing with machine learning techniques

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Djordje Cica ◽  
Branislav Sredanovic ◽  
Sasa Tesic ◽  
Davorin Kramar
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Sustainable Manufacturing ◽  
Cutting Speed ◽  
Statistical Error ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Learning Techniques

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

scholarly journals Development of Cost Estimation Models Based on ANN Ensembles and the SVM Method

2020 ◽  
Vol 30 (3) ◽  
pp. 48-67
Author(s):  
Michał Juszczyk
Keyword(s):  
Support Vector Machines ◽  
Cost Estimation ◽  
Construction Projects ◽  
Predictive Performance ◽  
Design Stage ◽  
Support Vector ◽  
Early Design Stage ◽  
Vector Machines ◽  
Structural Frames ◽  
The Cost

Abstract Cost estimation, as one of the key processes in construction projects, provides the basis for a number of project-related decisions. This paper presents some results of studies on the application of artificial intelligence and machine learning in cost estimation. The research developed three original models based either on ensembles of neural networks or on support vector machines for the cost prediction of the floor structural frames of buildings. According to the criteria of general metrics (RMSE, MAPE), the three models demonstrate similar predictive performance. MAPE values computed for the training and testing of the three developed models range between 5% and 6%. The accuracy of cost predictions given by the three developed models is acceptable for the cost estimates of the floor structural frames of buildings in the early design stage of the construction project. Analysis of error distribution revealed a degree of superiority for the model based on support vector machines.

scholarly journals Construction schedule risk analysis – a hybrid machine learning approach

2022 ◽  
Vol 27 ◽  
pp. 70-93
Author(s):  
John Patrick Fitzsimmons ◽  
Ruodan Lu ◽  
Ying Hong ◽  
Ioannis Brilakis
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Hybrid Method ◽  
Construction Projects ◽  
Gaussian Mixture ◽  
Machine Intelligence ◽  
Accurate Estimation ◽  
Support Vector ◽  
Risk Simulation ◽  
The Uk

The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.

scholarly journals Addressing IT Capacity Management Concerns Using Machine Learning Techniques

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hendrik Müller ◽  
Andrey Kharitonov ◽  
Abdulrahman Nahhas ◽  
Sascha Bosse ◽  
Klaus Turowski
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Capacity Management ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Effective Capacity ◽  
Support Vector ◽  
Practical Implementation ◽  
Management Service ◽  
Learning Techniques

AbstractThe digitization of various industries is emerging from and being supported by the Information Technology (IT) industry. However, bringing about the practical implementation of the fourth industrial revolution will require different fields of IT to undergo their own transformations. One of the research fields that draws a lot of attention nowadays is machine learning and its application in different areas. Therefore, in this paper, we present an analysis on the applicability of various machine learning techniques to address different problems in the field of capacity management for Commercial-off-the-shelf enterprise applications. Our investigation of the selected machine learning techniques is based on real monitoring data from over 18,000 SAP applications and database instances that are hosted on more than 16,000 different physical servers. These data are used to train various performance models, such as support vector machines with different kernels, random forests, and AdaBoost, for standard business functions. Boosted trees achieve sufficient accuracy to predict mean response times for ten frequently used transactions. To evaluate the suggested models, we applied them successfully to address different concerns in the context of capacity management. The evaluation includes multiple scenarios in the fields of server sizing, load testing, and server consolidation, with the objective to identify cost-effective designs. Based on the same monitoring data, we also present an anomaly detection scenario. In this scenario, we aim to demonstrate the use of machine learning techniques on historical data to detect possible performance anomalies for a suggested design or even predict possible anomalies in future scenarios. Results strongly emphasize the need to integrate monitoring data from standardized business applications to allow for novel and cost-effective capacity management service offerings.

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.

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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