Artificial Neural Network–Based Slip-Trip Classifier Using Smart Sensor for Construction Workplace

Lim, Tae-Kyung; Park, Sang-Min; Lee, Hong-Chul; Lee, Dong‐Eun

doi:10.1061/(asce)co.1943-7862.0001049

Cited by 57 publications

(23 citation statements)

References 38 publications

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“…Tripping hazards are among the most common incidents on construction sites (Lim et al 2015). Beyond the cost and loss of productivity resulting from these injuries, tripping accidents can lead to a lifetime of pain for injured workers.…”

Section: Resultsmentioning

confidence: 99%

“…Falls to the same level are one of the leading causes of occupational accidents in the United Kingdom, New Zealand, and the United States (e.g., Davis 2007;Cayless 2001;Bentley and Haslam 2001;Bentley et al 2003;Layne and Pollack 2004;HSE 2005;Lipscomb et al 2006;Yeoh et al 2013;BLS 2017). Moreover, non-fall slips and trips typically result in a large number of musculoskeletal injuries, which are among the costliest to treat (Lipscomb et al 2006;Lim et al 2015). Although incidents of different types of accidents overall are on the decline, the frequency of injuries due to slipping and tripping hazards is increasing, and their actual risks have been underestimated (Bentley and Haslam 2001;Kemmlert and Lundholm 2001;Layne and Pollack 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Although incidents of different types of accidents overall are on the decline, the frequency of injuries due to slipping and tripping hazards is increasing, and their actual risks have been underestimated (Bentley and Haslam 2001;Kemmlert and Lundholm 2001;Layne and Pollack 2004). As such, falls to the same level have become a major safety concern in both the manufacturing and construction industries (Lim et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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Examining the Relationship between Construction Workers’ Visual Attention and Situation Awareness under Fall and Tripping Hazard Conditions: Using Mobile Eye Tracking

Hasanzadeh

Esmaeili

Dodd

2018

J. Constr. Eng. Manage.

132

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The risk of major occupational accidents involving tripping hazards is commonly underestimated with a large number of studies having been conducted to better understand variables that affect situation awareness: the ability to detect, perceive, and comprehend constantly evolving surroundings. An important property that affects situation awareness is the limited capacity of the attentional system. To maintain situation awareness while exposed to tripping hazards, a worker needs to obtain feedforward information about hazards, detect immediate tripping hazards, and visually scan surroundings for any potential environmental hazards. Despite the importance of situation awareness, its relationship with attention remains unknown in the construction industry. To fill this theoretical knowledge gap, this study examines differences in attentional allocation between workers with low and high situation awareness levels while exposed to tripping hazards in a real construction site. Participants were exposed to tripping hazards on a real jobsite while walking along a path in the presence of other workers. Situation awareness was measured using the situation awareness rating technique, and subjects' eye movements were tracked as direct measures of attention via a wearable mobile eye tracker. Investigating the attentional distribution of subjects by examining fixation-count heat maps and scan paths revealed that as workers with higher situation awareness walked, they periodically looked down and scanned ahead to remain fully aware of the environment and its associated hazards. Furthermore, this study quantitatively compared the differences between the eye-tracking metrics of worker with different situation awareness levels (low versus high) using permutation simulation. The results of the statistical analysis indicate that subjects did not allocate their attention equally to all hazardous areas of interest, and these differences in attentional distribution were modulated by the workers' level of situation awareness. This study advances theory by presenting one of the first attempts to use mobile eye-tracking technology to examine the role of cognitive processes (i.e., attention) in human error (i.e., failure to identify a hazard) and occupational accidents.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Examining the Relationship between Construction Workers’ Visual Attention and Situation Awareness under Fall and Tripping Hazard Conditions: Using Mobile Eye Tracking

Hasanzadeh

Esmaeili

Dodd

2018

J. Constr. Eng. Manage.

132

View full text Add to dashboard Cite

show abstract

“…Other studies used ANNs to analyze and classify the risk of injury from machine and drilling [47], and the slip-trip incident risk in construction-related occupations [48].…”

Section: Artificial Neural Network In Occupational Safetymentioning

confidence: 99%

“…The work of Shankar Beriha et al [46] identified the deficiencies in safety health practices in Indian industries and analyzed the influence of workplace hazards on injury level and material damage. Other studies used ANNs to analyze and classify the risk of injury from machine and drilling [47], and the slip-trip incident risk in construction-related occupations [48].…”

Section: Artificial Neural Network In Occupational Safetymentioning

confidence: 99%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

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Predicting construction accidents on sites: An improved atomic search optimization algorithm approach

Liu,

Li,

Qiang

et al. 2023

Engineering Reports

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Construction accidents in the construction industry cause a large amount of property damage and human casualties. Therefore, avoiding construction accidents as much as possible is a problem that engineers have been working on for a long time. Currently, few construction managers use artificial intelligence methods for construction safety management. The purpose of this article is to propose a new artificial neural network (ANN) prediction model to provide early warning for future construction and to provide reference for construction organization decision‐makers. In the proposed method, atomic search optimization algorithm is used to optimize the weights and thresholds of back propagation neural network, and the Tent chaotic mapping is used to initialize the population to increase the diversity of the population. The statistical data of production safety accidents of housing and municipal engineering in China from 2015 to 2019 are used as an example, and the prediction results of the proposed model are compared with back‐propagation neural network (BPNN) and wavelet neural network (WNN). The mean absolute error (MAE) of predicting construction accidents is 0.2225, with small fluctuations in the predicted results. The mean absolute percentage error (MAPE) of the predictions is 0.6048%. The research results indicate that IASO‐BPNN has higher prediction accuracy than standard BPNN and WNN, providing judgment methods for ensuring construction progress and decision support for construction organization decision‐makers.

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Artificial Neural Network–Based Slip-Trip Classifier Using Smart Sensor for Construction Workplace

Cited by 57 publications

References 38 publications

Examining the Relationship between Construction Workers’ Visual Attention and Situation Awareness under Fall and Tripping Hazard Conditions: Using Mobile Eye Tracking

Examining the Relationship between Construction Workers’ Visual Attention and Situation Awareness under Fall and Tripping Hazard Conditions: Using Mobile Eye Tracking

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Predicting construction accidents on sites: An improved atomic search optimization algorithm approach

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