Learning From Accidents: Machine Learning for Safety at Railway Stations

Alawad, Hamad Ali H; Kaewunruen, Sakdirat; An, Min

doi:10.1109/access.2019.2962072

Cited by 52 publications

(21 citation statements)

References 53 publications

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“…The occupational accident data used in this study were collected from the database of the safety management system of a large construction company in Korea, for the period from 2015-2020; in total, 963 occupational accident data entries for the construction site were used. Since there are some studies using similar or small data in previous studies for accident analysis and prediction, the number of samples in this study is judged to be sufficient for machine learning [1,39,40]. However, the initial occupational accident dataset included too many factors, as well as over 130 occupational categories, and over 400 assailing materials.…”

Section: Initial Data and Data Descriptionmentioning

confidence: 99%

A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

Lee

Yoon

et al. 2020

Applied Sciences

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In the construction industry, it is difficult to predict occupational accidents because various accident characteristics arise simultaneously and organically in different types of work. Furthermore, even when analyzing occupational accident data, it is difficult to deduce meaningful results because the data recorded by the incident investigator are qualitative and include a wide variety of data types and categories. Recently, numerous studies have used machine learning to analyze the correlations in such complex construction accident data; however, heretofore the focus has been on predicting severity with various variables, and several limitations remain when deriving the correlations between features from various variables. Thus, this paper proposes a data processing procedure that can efficiently manipulate accident data using optimal machine learning techniques and derive and systematize meaningful variables to rationally approach such complex problems. In particular, among the various variables, the most influential variables are derived through methods such as clustering, chi-square, Cramer’s V, and predictor importance; then, the analysis is simplified by optimally grouping the variables. For accident data with optimal variables and elements, a predictive model is constructed between variables, using a support vector machine and decision-tree-based ensemble; then, the correlation between the dependent and independent variables is analyzed through an alluvial flow diagram for several cases. Therefore, a new processing procedure has been introduced in data preprocessing and accident prediction modelling to overcome difficulties from complex and diverse construction occupational accident data, and effective accident prevention is possible by deriving correlations of construction accidents using this process.

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Section: Initial Data and Data Descriptionmentioning

confidence: 99%

A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

Lee

Yoon

et al. 2020

Applied Sciences

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“…They presented that the developed approaches had the error of energy consumption calculation of less than 0.1 kWh and could reduce the energy consumption by 2.84%. Alawad et al [13] applied a decision tree to analyze fatal accidents. Sysyn et al [14] applied the computer vision concept to predict contact fatigue on crossings.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Sresakoolchai

Kaewunruen

2021

Vibration

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Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

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“…Of the many applications that have been applied to CNN, in this subsection, we present those that are specifically related to railways. Such studies have been widely reported in the recent literature and use many data sources; they cover management, maintenance, safety and operations [141]. Image-processing approaches for implementing automatic detection have been suggested for monitoring railway infrastructure [128], rail track maintenance [133], railway track inspections and train component inspections [142]- [152] such as the rolling bearings of trains [153].…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

“…When the prediction is positive and the ground-truth value is also positive, the prediction is called a true positive (TP). Similarly, false positive (FP), true negative (TN) and false negative (FN) values can be calculated [141]. These four values can be presented as a 2×2 contingency table, called confusion matrix, as depicted in Fig.…”

Section: ) the Evaluationmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

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Learning From Accidents: Machine Learning for Safety at Railway Stations

Abstract: enables a global research network that addresses the grand challenge of railway infrastructure resilience and advanced sensing in extreme environments (www.risen2rail.eu) [70].

Cited by 52 publications

References 53 publications

A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

A Deep Learning Approach Towards Railway Safety Risk Assessment

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