scholarly journals On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

Buildings ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 2 ◽  
Author(s):  
Michał Juszczyk
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Early Stage ◽  
Real Life ◽  
Percentage Error ◽  
Support Vector ◽  
Bridge Construction ◽  
Construction Costs ◽  
Cost Estimates ◽  
Comparable Performance

The completion of a bridge construction project within budget is one of the project’s key factors of success. This prerequisite is more likely to be achieved if the cost estimates, especially those provided in the early stage of a project, are realistic and close to the actual costs. The paper presents the research results on the development of a cost prediction model based on machine learning, namely the support vector machines (SVM) method, for which the input represents basic information and parameters of bridges, available in the early stage of projects. Several SVM-based regression models were investigated with the use of data collected for a number of bridge construction projects completed in Poland. Having finished the machine learning and testing processes, five of the models, of satisfying knowledge generalization ability and comparable performance, were preselected. The final selection of the best model was based on the comparison and analysis ability to predict bridge construction costs with accuracy appropriate for the early stage of projects. The general testing metrics of the finally selected model, named BCCPMSVR2, were as follows: root mean square error: 1.111; correlation coefficient of real-life bridge construction costs and costs predicted by the model: 0.980; and mean absolute percentage error: 10.94%. The research resulted in the development and introduction of an original model capable of providing early estimates of bridge construction costs with satisfactory accuracy.

scholarly journals Residential buildings conceptual cost estimates with the use of support vector regression

2018 ◽  
Vol 196 ◽  
pp. 04090 ◽  
Author(s):  
Michał Juszczyk
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Construction Projects ◽  
Residential Buildings ◽  
Kernel Functions ◽  
Support Vector ◽  
Cost Estimates ◽  
Cost Analyses ◽  
Machine Learning Methods ◽  
Machine Learning Tool

Cost analyses, and the conceptual cost estimates among them, are of the key importance for the construction projects successes. Implementation of neural networks or machine learning methods provides broad possibilities for this specific type of cost. The aim of the paper is to present some results of the studies on the use of support vector regression as a machine learning tool for conceptual cost estimates of residential buildings. Results for three models based on support vector regression and radial basis kernel functions are introduced.

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.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

scholarly journals Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2328 ◽  
Author(s):  
Md Shafiullah ◽  
M. Abido ◽  
Taher Abdel-Fattah
Keyword(s):  
Machine Learning ◽  
Fault Location ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Approach ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Performance Indices ◽  
Machine Learning Approach ◽  
Distribution Grids

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

2022 ◽  
Vol 12 (2) ◽  
pp. 828
Author(s):  
Tebogo Bokaba ◽  
Wesley Doorsamy ◽  
Babu Sena Paul
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Real Life ◽  
Support Vector ◽  
Road Traffic Accidents ◽  
Machine Learning Classifiers ◽  
Reduction Techniques ◽  
Learning Classifiers ◽  
Accident Data

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

scholarly journals Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Osama Siddig ◽  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
The Other ◽  
Well Logs ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Average Error ◽  
Support Vector ◽  
Empirical Correlations

Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods’ parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.

A Review of Machine Learning Techniques for Anomaly Detection in Static Graphs

2020 ◽  
pp. 146-162
Author(s):  
Hesham M. Al-Ammal
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Anomaly Detection ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Methods ◽  
Data Set ◽  
Learning Techniques ◽  
Vector Machines

Detection of anomalies in a given data set is a vital step in several applications in cybersecurity; including intrusion detection, fraud, and social network analysis. Many of these techniques detect anomalies by examining graph-based data. Analyzing graphs makes it possible to capture relationships, communities, as well as anomalies. The advantage of using graphs is that many real-life situations can be easily modeled by a graph that captures their structure and inter-dependencies. Although anomaly detection in graphs dates back to the 1990s, recent advances in research utilized machine learning methods for anomaly detection over graphs. This chapter will concentrate on static graphs (both labeled and unlabeled), and the chapter summarizes some of these recent studies in machine learning for anomaly detection in graphs. This includes methods such as support vector machines, neural networks, generative neural networks, and deep learning methods. The chapter will reflect the success and challenges of using these methods in the context of graph-based anomaly detection.

scholarly journals Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

2019 ◽  
Vol 11 (22) ◽  
pp. 2605 ◽  
Author(s):  
Wang ◽  
Chen ◽  
Wang ◽  
Li
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Soil Salinity ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Percentage Error ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Data Noise ◽  
Salt Affected Soil

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient (R2) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.

scholarly journals Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction

2019 ◽  
Vol 11 (14) ◽  
pp. 3828 ◽  
Author(s):  
Jin ◽  
Kim ◽  
Hyun ◽  
Han
Keyword(s):  
Life Cycle ◽  
Prediction Model ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Prediction Models ◽  
Early Stage ◽  
Case Based Reasoning ◽  
Probabilistic Prediction ◽  
Construction Costs ◽  
Early Stages

Decisions made in the early stages of construction projects significantly influence the costs incurred in subsequent stages. Therefore, such decisions must be based on the life-cycle cost (LCC), which includes the maintenance, repair, and replacement (MRR) costs in addition to construction costs. Furthermore, as uncertainty is inherent during the early stages, it must be considered in making predictions of the LCC more probabilistic. This study proposes a probabilistic LCC prediction model developed by applying the Monte Carlo simulation (MCS) to an LCC prediction model based on case-based reasoning (CBR) to support the decision-making process in the early stages of construction projects. The model was developed in two phases: first, two LCC prediction models were constructed using CBR and multiple-regression analysis. Through k-fold validation, one model with superior prediction performance was selected; second, a probabilistic LCC model was developed by applying the MCS to the selected model. The probabilistic LCC prediction model proposed in this study can generate probabilistic prediction results that consider the uncertainty of information available at the early stages of a project. Thus, it can enhance reliability in actual situations and be more useful for clients who support both construction and MRR costs, such as those in the public sector.

Sign in / Sign up

Export Citation Format

Share Document