scholarly journals A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

2020 ◽  
Vol 10 (20) ◽  
pp. 7153 ◽  
Author(s):  
Ehsan Harirchian ◽  
Vandana Kumari ◽  
Kirti Jadhav ◽  
Rohan Raj Das ◽  
Shahla Rasulzade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Safety Evaluation ◽  
Support Vector ◽  
Optimum Number ◽  
Damage Classification ◽  
Damage Prediction ◽  
Rapid Visual Screening ◽  
Damage Data ◽  
Screening Platform

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

scholarly journals Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3340 ◽  
Author(s):  
Ehsan Harirchian ◽  
Tom Lahmer ◽  
Vandana Kumari ◽  
Kirti Jadhav
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Risk Mitigation ◽  
Safety Evaluation ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Economic Losses ◽  
Support Vector ◽  
Optimum Number ◽  
Damage Classification

The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.

scholarly journals Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3532 ◽  
Author(s):  
Nicola Mansbridge ◽  
Jurgen Mitsch ◽  
Nicola Bollard ◽  
Keith Ellis ◽  
Giuliana Miguel-Pacheco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Budget ◽  
Learning Algorithms ◽  
Eating Behaviour ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Optimum Number ◽  
Eating Behaviours ◽  
Adaptive Boosting

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

scholarly journals Computational prediction of implantation outcome after embryo transfer

2019 ◽  
Vol 26 (3) ◽  
pp. 1810-1826 ◽  
Author(s):  
Behnaz Raef ◽  
Masoud Maleki ◽  
Reza Ferdousi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Model ◽  
Embryo Transfer ◽  
Area Under The Curve ◽  
Computational Prediction ◽  
Support Vector ◽  
Human Menopausal Gonadotropin ◽  
Optimum Number ◽  
Learning Approaches

The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

Damage Classification of Composites Using Machine Learning

2019 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Machine Learning ◽  
Composite Materials ◽  
Random Forest ◽  
Condition Monitoring ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Damage Classification ◽  
Combining Data ◽  
Ultrasonic Measurements

Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

scholarly journals The use of machine learning in rare diseases: a scoping review

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Julia Schaefer ◽  
Moritz Lehne ◽  
Josef Schepers ◽  
Fabian Prasser ◽  
Sylvia Thun
Keyword(s):  
Machine Learning ◽  
Rare Disease ◽  
Rare Diseases ◽  
Scoping Review ◽  
Input Data ◽  
Skin Diseases ◽  
Demographic Data ◽  
Support Vector ◽  
Research Activity ◽  
Data Set

Abstract Background Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. Methods Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010–2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. Results Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and “omics” data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). Conclusion Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

Optimal Model Selection of Suport Vector Classifiers for Rolling Element Bearings Fault Detection Using Statistical Time-Domain Features

2008 ◽  
Author(s):  
Z. Hameed ◽  
Y. S. Hong ◽  
Y. M. Cho ◽  
S. H. Ahn ◽  
C. K. Song
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Time Domain ◽  
Input Data ◽  
Good Choice ◽  
Abnormal Behavior ◽  
Support Vector ◽  
Optimal Model ◽  
Super Computer ◽  
Statistical Time

Support Vector Machines (SVMs) are being used extensively now days in the arena of pattern recognition and regression analysis. It has become a good choice for machine learning both for supervised and unsupervised learning purposes. The SVM is primarily based on the mapping the data to a hyperplane using some kernel function and then increasing the margin between the hype planes so this hyperplane classifies the data in the normal and fault state. Due to large amount of input data, it is computationally cumbersome to yield the desired results in shortest possible time by using SVM. To overcome this difficulty in this work, we have employed statistical Time-Domain Features like Root Mean Square (RMS), Variance, Skewness and Kurtosis as pre-processors to the input raw data. Then various combinations of these time-domains signals and features have been used as inputs and their effects on the optimal model selection have been investigated thoroughly and optimal one has been suggested. The procedure presented here is computational less expensive otherwise to process the input data for model selection we may have to use super computer. The implementation of proposed method for machine learning is not much complicated and by using this procedure, an impending fault/abnormal behavior of the machine can be detected beforehand.

scholarly journals A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

2021 ◽  
Vol 11 (16) ◽  
pp. 7540
Author(s):  
Ehsan Harirchian ◽  
Vandana Kumari ◽  
Kirti Jadhav ◽  
Shahla Rasulzade ◽  
Tom Lahmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Mitigation ◽  
Rapid Screening ◽  
Screening Methods ◽  
Learning Approaches ◽  
Vast Number ◽  
Rapid Visual Screening ◽  
Damage Grades ◽  
Damage Data ◽  
Actual Damage

A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.

What can machine learning do for seismic data processing? An interpolation application

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. V163-V177 ◽  
Author(s):  
Yongna Jia ◽  
Jianwei Ma
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Input Data ◽  
Interpolation Method ◽  
Window Size ◽  
Tight Frame ◽  
Training Data ◽  
Low Rank ◽  
Support Vector ◽  
Data Sets

Machine learning (ML) systems can automatically mine data sets for hidden features or relationships. Recently, ML methods have become increasingly used within many scientific fields. We have evaluated common applications of ML, and then we developed a novel method based on the classic ML method of support vector regression (SVR) for reconstructing seismic data from under-sampled or missing traces. First, the SVR method mines a continuous regression hyperplane from training data that indicates the hidden relationship between input data with missing traces and output completed data, and then it interpolates missing seismic traces for other input data by using the learned hyperplane. The key idea of our new ML method is significantly different from that of many previous interpolation methods. Our method depends on the characteristics of the training data, rather than the assumptions of linear events, sparsity, or low rank. Therefore, it can break out the previous assumptions or constraints and show universality to different data sets. In addition, our method dramatically reduces the manual workload; for example, it allows users to avoid selecting the window size parameters, as is required for methods based on the assumption of linear events. The ML method facilitates intelligent interpolation between data sets with similar geomorphological structures, which can significantly reduce costs in engineering applications. Furthermore, we combine a sparse transform called the data-driven tight frame (so-called compressed learning) with the SVR method to improve the training performance, in which the training is implemented in a sparse coefficient domain rather than in the data domain. Numerical experiments show the competitive performance of our method in comparison with the traditional [Formula: see text]-[Formula: see text] interpolation method.

scholarly journals Safety Evaluation of a Water-Immersed Bridge Against Multiple Hazards via Machine Learning

2019 ◽  
Vol 9 (15) ◽  
pp. 3116
Author(s):  
Kuo-Wei Liao ◽  
Fu-Sheng Chien ◽  
Rong-Jing Ju
Keyword(s):  
Machine Learning ◽  
Fragility Curves ◽  
Plastic Hinge ◽  
Pushover Analysis ◽  
Safety Evaluation ◽  
Time History ◽  
Seismic Intensity ◽  
Support Vector ◽  
Stream Velocity ◽  
Analysis Model

A safety analysis process for an immersed bridge is proposed, in which material nonlinear properties, added mass, soil spring, plastic hinge length and property, nonlinear pushover analysis, and time history analysis are included. Outcomes of interest in a bridge analysis, such as displacement ductility, is not an easy task if the authentic model is adopted. The least-squares support-vector machine (LSSVM) is therefore proposed for replacing the analysis model. Water-immersed bridges under varied water depths, scouring depth, and seismic intensity are investigated. The feasibility of using machine learning as a surrogate model is discussed. Results indicate that the trends of fragility curves derived from LSSVM are very similar to those of the authentic model. Therefore, LSSVM is a suitable tool to reduce the computational burden. Analyses also show that stream velocity and pier length are two important factors for the safety of an immersed bridge, and the immersed effect should not be ignored if pier length is long.

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