Elsevier

Automation in Construction

Volume 62, February 2016, Pages 101-113
Automation in Construction

Development of an early-warning system for site work in hot and humid environments: A case study

https://doi.org/10.1016/j.autcon.2015.11.003Get rights and content

Highlights

  • An early-warning system for working in hot and humid environment is developed.

  • Workers' heat strain level can be monitored when they perform tasks in hot weather.

  • Health alert messages with corresponding intervention measures can be prompted.

Abstract

This study presents an early-warning system for working in hot and humid environment. The developed system can monitor workers' heat strain level when they have to work under such hostile conditions continuously. Health alert messages with corresponding intervention measures will be prompted to workers to safeguard their wellbeing. Heat strain is evaluated by a subjective index perception rating of perceived exertion (RPE) and an objective heat strain indicator heart rate. A database containing 550 sets of synchronized work-related, environmental, and personal data were used to construct the prediction model. Artificial neural networks were applied to forecast the RPE of construction workers. Statistical measures including MAPE, RMSE and R2 confirm that the established model is good fitting with high accuracy. The proposed system could be automated by integrating smart sensor technology, location tracking technology, and information communication technology, which could be in the form of GSM based environmental sensor, smart bracelet, and smart phone application, to protect the wellbeing for those who have to work in hot and humid conditions.

Introduction

Construction is a large, complex, and dynamic sector that generates employment for millions of people worldwide. However, this sector has the most fatalities and high incidence of non-fatal occupational injuries and illnesses on days away from work. Based on the estimates of International Labour Organization (ILO), at least 60,000 fatal accidents occur each year in construction sites around the world, which represent one fatal accident every 10 min [1]. In addition, the ILO estimates that the construction workers in industrialized countries are 3 to 4 times greater than other employees to die from accidents at work [2]. Aside from the dangers of being the front-liners on a jobsite, workers in the construction industry also confront potential health hazards (e.g., temperature extremes, radiation, chemicals, dusts, vibration, and noise) throughout the building process. Furthermore, about 30% of construction workers in some European countries suffer from pain and musculoskeletal disorders [3]. The occupational illnesses of construction workers have not been accurately measured, but an educated guess indicates that construction workers suffer both acute (short-term) and chronic (long-term) illnesses from their exposure to environmental hazards [4]. The health and safety of construction workers aroused greater attention from governments, industry community practitioners, and the academia.

Heat stress is a well-known occupational hazard in the construction industry [4], and climate change together with the increased frequency and intensity of extreme heat events have made risks more severe and widespread [5], [6], [7], [8]. Heat stress causes physiological and psychological discomforts, deteriorates performance and productivity, increases incident rates, and even threatens survival [9], [10], [11]. Increased thermoregulatory, cardiovascular and perceptual strains on the body promote confusion, irritability, and other emotional stress, which may cause workers to distract attention from tasks or ignore safety procedures [12]. Published reports in the United States suggest that work in construction occupation is associated with an increased risk of heat-related death and illness [13], [14]. In Japan, a total of 47 deaths due to heat stroke were reported as industrial accidents in hot environments during 2010. The construction industry is found to be more susceptible to heat stress than other industries, which is accounted for 64% of all lethal cases [15]. News reports archived in Hong Kong show alarming incidences of heat stress and verifiable reported deaths in the construction industry [16]. A recent survey revealed that 5% of construction workers had suffered from heat stroke and 23% had experienced symptoms of heat stroke [17]. In Taiwan, construction workers were identified as the most vulnerable population in which high temperature impacts on health and productivity [18]. The risk of heat stress experienced by construction workers may even be higher in the Middle East where ambient air temperatures often reach 45 °C and higher with 90% humidity [19]. Considering the high frequency of heat-related incidents in the construction industry, understanding how heat stress results in heat-related illnesses and how it affects construction workers is important in planning for intervention strategies.

To prevent heat stress, a series of fundamental practice notes and guidelines (e.g., appropriate work arrangements, shelters at work or rest places to reduce radiant heat gain, ventilation in indoor working environment, air-conditioned rest rooms, and provision of drinking water or sports drinks) have been promulgated [20], [21], [22], [23]. Likewise, the issue of working under hot weather has been a concern of academic researchers. The limits of human tolerance in terms of physiological parameters (e.g., core temperature, heart rate, skin temperature) have been evaluated at different levels of heat exposure [24], [25], [26]. Upper tolerance limits in terms of environmental indicators (e.g., temperature, humidity, wet bulb globe temperature, thermal work limit) have been explored in literature [27] and adopted by regulatory organizations [28], [29], [30]. Recently, attempts and efforts were made to establish a safety evaluation model for assessing the risk of heat stress in the workplace. Evaluating safety is not only an important means for implementing the policy but also provides a base for establishing a scientific and standardized management of enterprises [31]. For example, Ren et al. [32] established an evaluation framework for assessing the hazards of heat stress in the workplace in an underground mine. Zheng et al. [33] studied safety evaluation and early warning rating of hot and humid environment. However, no systematic and in-depth studies have been conducted with respect to safety evaluation and early warning in hot and humid environment for the identification, evaluation, control, and management of human behavioral factors. The performance of work activities befitting safety and accident prevention is a continuing and dynamic process. The key to achieving these objectives lies with workers' concentration. The cognitive condition of concentration can be viewed through the concept of mindfulness [34]. Mindful work organization and performance can be achieved through the improvement of workers' alertness to and awareness of the hazardous nature of the operations.

Occupational heat strain results from a combination of factors, which include environmental conditions, work demands, and individual characteristics. Earlier studies by Chan et al. [35], [36] established a multiple linear model (MLR) to predict a worker's heat strain to different environmental factors, work-related factors, and personal factors. However, it was challenged that heat stress and heat strain may not be linearly correlated [37]. Furthermore, the combination of the large array of personal health and lifestyle factors and their complex interaction effects is far beyond the predictive power of a MLR [38]. More advanced analytical techniques are required to tackle these complex issues. Artificial neural networks (ANNs), a form of artificial intelligence technique, is one such approach which provides a high level of flexibility and competency in nonlinearities and complex behavior. ANN provides solutions to many complex problems in biology and medicine that are beyond the computational capacity of classical mathematical and traditional statistical techniques [39]. The application of ANN in data treatment is high, particularly where systems present nonlinearities and complex behavior [40].

Innovative devices and technologies [e.g., physiological status monitor (PSM) and Ultra-Wideband (UWB)] are being used to real-time location tracking and physiological status of construction workers for enhancing construction safety and productivity [41], [42], [43], [44], [45], [46], [47]. This research aims to develop an early-warning system for construction workers against hot and humid climates using modern technologies and ANN as the principal analytical technique. The objectives of this study are to (1) develop a model to predict a worker's heat strain in hot and humid environment; and (2) identify proper precautions against the hazards and risks in hot and humid environment to prevent and reduce the harmful effects of heat exposure. Since Hong Kong is in a subtropical climate zone where air temperature typically reaches 34.5 °C on its hottest summer days [48] and business environment of the construction industry in Hong Kong is highly competitive [49], the Hong Kong construction industry is selected as a prototype for developing a more focused methodology which if successful could also be applied to other regions.

Section snippets

Heat strain assessment

Heat stress is defined by National Institute for Occupational Safety and Health as the sum of the heat generated in the body (metabolic heat) plus the heat gained from the environment minus the heat lost from the body to the environment [30]. Heat strain describes the overall physiological and psychological response resulting from heat stress [50]. International Standard Organization has identified the indicators for heat strain, including body core temperature, skin temperature, heart rate and

Material and methods

To construct the heat stress model, field studies were conducted on six construction sites during the summer time in Hong Kong (July to September in 2010 and 2011) to collect the necessary dataset.

Results

An ANN model using Levenberg–Marquart back propagation was used on the database. This model was trained using 384 randomly selected samples; the remaining 166 samples were equally divided for the ANN validation and testing process. All performance measure values confirmed a good-fit and a robust model. As shown in Fig. 7, the values obtained through the training, validation, and testing of ANN model are identical with measured data, implying the input parameters were strongly correlated with

Validation of the early-warning system

In order to validate the early warning system, a controlled experiment was conducted during the summer time in a construction site in Hong Kong. A 45 years old sheet metal worker with BMI of 23.3%, who smokes and drinks occasionally, was invited to participate. An app of the proposed system was built on iPhone platform (Fig. 8A). Prior to the test, the worker input the personal information (i.e., age, height, weight, smoking habit, and alcohol drinking habit) and job nature into the system (

Discussion

This study adopted RPE as a yardstick to evaluate the risk of heat stress that the construction workers suffered. Many reports have been published concerning RPE during exercise in the heat. RPE is regarded as an essential indicator to investigate the physical strain in hot environments. Previous studies of exercise performance in the hot environments demonstrated that the RPE is linearly correlated with heat strain indicator (e.g., body temperature, heart rate, sweating rate) [83], [84],

Conclusions

Construction workers are at a high risk of exposure to heat stress as they undertake physically demanding tasks. This study has established an algorithm to develop an early-warning system against hot and humid climates, which can alert workers to occupational heat stress and provide intervention measures as well. In this regard, the study contributes to literature by filling the research gap arising from the limited number of studies in safety evaluation in hot and humid environment. This study

Acknowledgments

This project is funded by two grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC Project No. PolyU510409 and PolyU510513). The support from the Hong Kong Polytechnic University's Institute of Textiles and Clothing (ITC) is deeply appreciated. The research team is also indebted to the technical support from technicians of the Hong Kong Polytechnic University and the Hong Kong Institute of Education. In particular, the participation of volunteers in

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