A heatstroke prediction and prevention system for outdoor construction workers
© Yabuki et al.; licensee Springer. 2013
Received: 10 February 2013
Accepted: 10 September 2013
Published: 3 October 2013
Recently, the number of heatstroke cases is increasing among construction workers. To prevent heatstroke at construction sites, it is necessary to accurately predict both the thermal environment of construction sites and the physiological condition of workers, which is presently difficult to achieve.
We have therefore developed the Construction Workers’ Heatstroke Prevention (CWHP) system specifically for outdoor construction workers. The CWHP system consists of the Thermal Environment Prediction (TEP) system, which predicts changes in the thermal environment based on forecast values obtained from the Japan Meteorological Agency and results of computational fluid dynamics and solar insolation simulations, and the Core Body Temperature Prediction (CBTP) system, which predicts changes in worker core body temperature based on the TEP system results. The CWHP’s heatstroke risk notification system indicates the risk of worker heatstroke considering the work site and schedule, so that construction managers can appropriately schedule work or install appropriate facilities such as tents, electric fans, and cold water tanks before work starts. The system is flexible enough to accommodate situations differing from meteorological agency forecasts.
In summer 2011 the CWHP system was applied at Osaka University campus under hypothetical situations using the Virtual Reality Avatar Thermal Environment (VRATE) system, previously developed by the authors.
The system accurately predicted the time history of construction worker core body temperatures and informed users of times of heatstroke risk, allowing work sites and worker schedules to be modified such that new plans showed no risk for that day.
KeywordsHeatstroke Computational fluid dynamics Construction worker VR Thermal environment Core body temperature
In recent years, heatstroke has become a serious problem in summer in Japan, where the annual number of heatstroke cases has exceeded 20,000 since 2006, and is increasing yearly. In 2010 Japan experienced an extreme heat wave, during which heatstroke resulted in 56,064 hospitalizations and 1,718 deaths (Ministry of Health, Labor and Welfare MHLW 2011). Heatstroke occurs when the body temperature rises abnormally because of high temperature and humidity. About 70% of occupational heatstroke fatalities occur in the construction industry. It is well known that to prevent heat-related illnesses workers should drink plenty of water, rest frequently in shaded or air-conditioned areas, and wear protective clothing that provides cooling (Occupational Safety and Health Administration OSHA 2011). Once a worker indicates symptoms of heatstroke, immediate medical assistance should be requested, the worker should be moved to a cool, shaded area, and excess clothing should be removed and cool water should be applied to the body (National Institute for Occupational Safety and Health NIOSH 2010). However, as a construction site is a severe thermal environment in summer, risk of heatstroke should be evaluated considering the thermal environment and human physiology.
The construction industry has problems with health and safety (Paton 2009), and a number of studies have examined ways to improve the health and safety situations (Hastle et al. 2009 McDonald et al. 2009 Sacks et al. 2009). The application of virtual reality and 3D models to health and safety problems, especially to falls from height, has been explored by a number of researchers (Dawood et al. 2012 Zhang et al. 2013), as has research on heat stress in construction and work environments (Yi and Chan 2013 Ashraf and Naseem 2003 Tien et al. 2011). Computational Fluid Dynamics (CFD) analysis has been used extensively to simulate the thermal environment of spaces such as rooms, offices, buildings, urban areas, and global areas. Various studies have used CFD analysis to interpret and mitigate urban heat island phenomena (Ashie et al. 2007 Jiang and Hoyano 2008).
The risk of heatstroke becomes high when the core body temperature rises to about 39°C. Equations for estimating core body temperature, described in the next section, have been proposed by Kubota et al. (2003). To evaluate risks of heatstroke in the thermal environment, wet bulb globe temperature (WBGT) is often used (Dang et al. 2006). WBGT is a composite of natural wet-bulb temperature, globe thermometer temperature, and dry-bulb temperature. However, heatstroke onset depends largely on the physiological condition of the individual. Wearable thermal sensors can measure body temperature and heat rate, but failure to check sensor readings or ignoring alarms can lead to failure of such measures to prevent heatstroke. Moreover, environmental factors such as air temperature, humidity, and solar radiation significantly vary from place to place.
The authors aim to develop an individualized core body temperature prediction method for construction workers based on physiological conditions and chronological work schedule. Predictions of high risk of heatstroke would then allow work schedules to be modified beforehand. This research focuses on outdoor construction workers. To develop such a system, therefore, the thermal environment of the construction site and its surrounding area should be predicted thoroughly by the CFD analysis, and the core body temperature of each worker should be computed by the predicted thermal environment data and the individual worker’s physiological condition. Furthermore, this process should be executed quickly, because a series of re-computations may be needed when heatstroke risk is identified and re-scheduling is necessary. Since CFD analysis usually takes hours or even days, a new method that more quickly predicts the thermal environment is needed. In addition, the actual thermal environment may significantly deviate from computations and predictions due to climate change. Real time monitoring of the thermal environment is therefore necessary to evaluate differences from the predicted data.
No methods have been developed that satisfy these requirements. The objective of this research is therefore to develop a heatstroke prevention system for outdoor construction workers using and integrating information and communication technology and simulation technologies.
Construction Workers’ heatstroke prevention system
This section describes the Construction Workers’ Heatstroke Prevention (CWHP) system. This system predicts the thermal environment of a construction site and the core body temperature of site workers, thereby predicting the heatstroke risk for that day.
Overview of the CWHP system
In the TEP system, thermal environmental data at the construction site are obtained from sensors, and JMA daily forecasts regarding local high and low temperatures and barometric patterns are obtained via the Internet. Thermal environment changes are strongly related to pressure patterns, so the TEP system predicts site temperatures based on statistical patterns in local monitoring data from the Automated Meteorological Data Acquisition System (AMEDAS) of the Japan Meteorological Agency (JMA).
The CBTP system next predicts changes in the core body temperature of each construction worker, based on individual physiological data and the thermal environment data predicted by the TEP system. The risk of heatstroke is evaluated according to core body temperature.
Finally, the heatstroke risk notification system reports when and where construction workers will experience heatstroke risk, allowing site construction managers to change work sites and daily schedules as appropriate.
Thermal environmental prediction system
To estimate the risk of heatstroke it is necessary to forecast changes in the thermal environment, including temperature, humidity, wind direction, wind velocity, and insolation for several hours. Predicted thermal environmental data can then be used to calculate body heat loads. However, since CFD analysis usually takes from hours to days, a much faster system should be developed to predict thermal environment change. We thus propose a method for quickly predicting construction site thermal environments by developing the TEP system, which can forecast the thermal environment based factors such as barometric pressure patterns and local sensing data.
Pressure pattern selection
Pressure patterns and their characteristics
1) Summer type
Country is covered by a Pacific Ocean high pressure system and fine, humid weather continues.
2) Front type
Rain and clouds due to the front
3) Unsettled weather type
High and low pressure systems come and go frequently and abrupt climate changes occur.
4) Typhoon type
Heavy rain and strong wind are expected due to the typhoon.
Prediction of thermal environment change
T'' max : Maximum temperature predicted by Meteorological Agency [°C]
T max : Average maximum temperature [°C]
T'' min : Minimum temperature predicted by forecast [°C]
T min : Average minimum temperature [°C]
n: Time [−]
r i : Temperature difference between the average and modified curves at time i [°C]
Thermal environment prediction at and around a construction site
Performing CFD analysis using sensor data as input would be ideal for predicting construction site thermal environments, but CFD analysis typically requires from several hours to several days. The proposed method thus performs a number of CFD analyses and insolation simulations for various situations beforehand, and tabulates differences in thermal environment data between benchmark points and other locations in the analysis area as factors. At the construction site, environmental sensors are installed at the benchmark point, and a previous simulation similar to the sensed data is selected. The factors are used to predict the thermal environment data at and around the construction site.
CFD analysis conditions
Dirichlet Boundary Condition
17 August 2011
Normal Plane Direct Solar Radiation
Neumann Boundary Condition
Inflow and Outflow Condition of Boundary and Internal Areas
Restriction of Re-entry from the Outside of the Internal Area
Restriction of Inflow from the Boundary Area
The thermal environment may change significantly due to sudden climate change, thus deviating from the estimated values. In such cases, the system changes the estimated pattern from the initial middle curve to a more appropriate one if the deviation exceeds five minutes. This adaptation mechanism gives the system flexibility under uncertain thermal environments.
Core body temperature prediction system
The CBTP system predicts the core body temperature of each construction worker, based on the estimated thermal environment data obtained by the TEP system, the worker’s physiological data, and planned work site location information. The system then predicts the probable time of heatstroke onset, if heatstroke is anticipated. The risk of heatstroke is very high when the core body temperature reaches 39°C.
ΔTre: Change in body temperature during the day [°C]
AD: Total surface area of body [m2]
W t : Body weight [kg]
M: Metabolism [W/m2] (ASHRAE 2009)
W: External work [W/m2]
C res : Heat by respiratory convection flow [W/m2]
E res : Respiratory heat of vaporization [W/m2]
C: Heat by convection [W/m2]
R: Heat by radiation [W/m2]
E: Heat by vaporization [W/m2]
ΔTsk: Change in average skin temperature [°C]
h c : Convection heat transfer coefficient [W/m2/K]
v a : Wind velocity [m/s]
P a : Water vapor pressure [Pa]
T s : Average skin temperature [°C]
T a : Air temperature [°C]
h r : Radiation heat transfer coefficient (4.65 [W/m2/K])
T r : Average radiation temperature [°C],
K ↓: Amount of global solar radiation [W/m2]
L ↓: Amount of atmospheric radiation [W/m2],
T g : Ground surface temperature [K]
f: Water vapor pressure [mmHg]
E(T a ): Amount of saturated water vapor at Ta [g/m3]
σ : Stefan-Boltzmann constant (5.67×10−8[W/m2/K4])
RH: Relative humidity [%]
F pcl : Moisture transmission efficiency of cloth [−]
P sk *: Water vapor partial pressure from skin saturated at skin temperature Tsk [Pa]
ω: Ratio of wet skin on body [−]
A W : Wet skin area [m2]
Physiological data such as weight, height, and body temperature for each worker, and planned work schedules including location and job description are input into the system. The TEP system provides the thermal environment data at the work place. Metabolism information for each job, based on a table proposed by ASHRAE (2009), is stored in the CBTP system database. The CBTP system computes worker core body temperatures for each minute, based on initial conditions, and displays the result to the user.
Heatstroke risk notification system
The heatstroke risk notification system receives worker core body temperature data from the CBTP system. If a worker’s core body temperature is predicted to reach 39°C at some point in the simulation, the user can be notified of the time of probable heatstroke onset. If heat stroke risk is anticipated, the construction manager should modify the work site and schedule until the risk is eliminated.
Application of the CWHP system
This chapter demonstrates how the CWHP system can be applied to a construction site.
A prototype system was developed based on the proposed methodology, and an experiment was performed at and around the Techno Alliance Building of Osaka University in summer 2011. The software packages 3ds Max (Autodesk) and 3DVIA Virtools 5.0 (Dassault Systèmes) were employed to develop the prototype system. Building construction began in 2010 and completed in April 2011. However, it was hypothetically assumed that the building was still under construction for the experiment. The experiment was performed in a virtual world, in which a realistic construction site on a hot summer day was replicated based on in-situ monitored data.
Thermo-boxes are centered on the avatar at 500 mm intervals, and show the thermal environment in a 2,000 × 2,000 × 2,000 mm area. Thermo-boxes turn red as the thermal index increases, and blue if the thermal index is low. Particles are small objects generated from a particle generation object based on the avatar’s surroundings. The particle generation objects, which are not displayed in the VR space, are arranged at 1,000 mm intervals in the avatar’s surroundings. Particle colors indicate the thermal index in the manner of thermo-boxes, and particle movement shows the wind speed and direction (particles are modeled as very light materials, so wind speed and direction are faithfully reflected). Both methods can be used simultaneously, with thermo-box and particle colors indicating thermal index and particle movement indicating wind speed and direction. The visualization flow is as follows: first, positional coordinates of the avatar and its surroundings are acquired. Next, the nearest positional coordinate is found, and the thermal environment information and thermal index of that coordinate is sampled. In case of a particle, particle generation objects change direction, reflecting wind information. Thermo-box and particle colors are then modified according to thermal index, and a particle is generated from the particle generation object.
CFD analysis and solar insolation simulation
Observation of thermal environment
Range of measurement
−10 – 45°C
Sumitomo Precision Products Co., Ltd.
30 – 80%
Sumitomo Precision Products Co., Ltd.
Radiation Balance Measurement (CN-11)
0 – 2,000 W/m2
EKO Instruments Co., Ltd.
Model 03002 V Wind Sentry
0.5 – 60 m/s
+/− 0.3 m/s
Amalgamated Instrument Co., Pty., Ltd.
Model 03003 V Wind Sentry
0 – 360°
Amalgamated Instrument Co., Pty., Ltd.
Results and discussion
Example applications of the CWHP system using the VRATE system
Average skin temperature
Core Body temperature
Amount of perspiration
Computed using the two-node model by Gagge et al. (1971)
A hypothetical construction work order
08:00 – 11:00
Transport building materials
11:00 – 12:00
Check building materials
12:00 – 13:00
13:00 – 18:00
The result of the CWHP system application shows that the core body temperature increased about 1°C from 8:00 to 12:00 by the work of transporting and checking building materials at work area A. During the 12:00 to 13:00 lunch break, core body temperature decreased only slightly. During the afternoon scaffolding work, core body temperature increased about 1°C to almost 39°C at around 16:00, indicating the construction worker might have experienced heatstroke onset at that time.
In the next scenario, in the morning the avatar worked at work area B, transporting and checking building materials as in the previous case, and performed scaffolding work in area C in the afternoon. Another case variation was also executed in which a sunshade tent was installed at work area A. The result shows as the work area B is in the shade because of trees the temperature there is lower than in work area A, so the core body temperature increased only about 0.5°C in the morning. As for the sunshade case, the core body temperature increased about 0.7°C in the morning. Both modifications are considered to be effective for mitigating heatstroke risk. However, changing the work areas is more effective in this case because work area B is surrounded by trees and the ground is covered with grass.
A modified hypothetical construction work order
08:00 – 09:00
Transport building materials
09:00 – 10:00
Check building materials
10:00 – 12:00
12:00 – 13:00
13:00 – 16:00
16:00 – 18:00
Another modified hypothetical construction work order
08:00 – 11:00
Transport building materials
11:00 – 12:00
Check building materials
12:00 – 13:00
13:00 – 14:00
14:00 – 16:00
16:00 – 18:00
The CWHP system was developed for outdoor construction sites. This system is composed of the TEP system, the CBTP system, and the heatstroke risk notification system. The TEP and CBTP systems were implemented. The TEP system can predict the thermal environment data of all places within the modeled area before work begins in the morning and can flexibly change the predicted data, based on the sensing data at a benchmark point at the site. The CBTP system can predict daily time-series data for construction worker core body temperature, based on the work schedule. We applied the CBTP system to a hypothetical construction site at Osaka University, using the VRATE system and found that this system could predict the core body temperature, which varies throughout the day, and predict the risk of heatstroke according to core body temperature for each time zone. Since the core body temperature changes according to work location, task performed, and individual amounts of perspiration, we confirmed that the evaluation method considers physiological aspects of workers and modification of work schedules.
For future work, a method to convert building information models in and around the site into CFD analysis 3D models should be developed to reduce time and work for making 3D data for CFD analysis. Since in the current TEP system the shaded areas receive no direct solar radiation, partial shading should be incorporated in the future. The heatstroke risk notification system should be implemented using smartphones. If a construction work schedule is changed in the morning because workers receive heatstroke risk warnings, construction managers would want to know how changes will impact the construction job. This could be an interesting research theme, where four- and five-dimensional models (McKinney et al. 1996) could be employed. Finally, the system should be applied to an actual construction site to verify system efficacy.
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