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Sensors 2018, 18, 3897; doi:10.3390/s18113897 www.mdpi.com/journal/sensors
Article
Sensor-Based Safety Performance Assessment of
Individual Construction Workers
JeeWoong Park 1,*, Yong K. Cho 2 and Ali Khodabandelu 3
1 Department of Civil and Environmental Engineering and Construction, The University of Nevada,
Las Vegas, NV 89154, USA
2 School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;
yong.cho@ce.gatech.edu
3 Department of Civil and Environmental Engineering and Construction, The University of Nevada,
Las Vegas, NV 89154, USA; khodaban@unlv.nevada.edu
* Correspondence: jee.park@unlv.edu
Received: 12 October 2018; Accepted: 6 November 2018; Published: 12 November 2018
Abstract: Over the last decade, researchers have explored various technologies and methodologies
to enhance worker safety at construction sites. The use of advanced sensing technologies mainly has
focused on detecting and warning about safety issues by directly relying on the detection
capabilities of these technologies. Until now, very little research has explored methods to
quantitatively assess individual workers’ safety performance. For this, this study uses a tracking
system to collect and use individuals’ location data in the proposed safety framework. A
computational and analytical procedure/model was developed to quantify the safety performance
of individual workers beyond detection and warning. The framework defines parameters for zone-
based safety risks and establishes a zone-based safety risk model to quantify potential risks to
workers. To demonstrate the model of safety analysis, the study conducted field tests at different
construction sites, using various interaction scenarios. Probabilistic evaluation showed a slight
underestimation and overestimation in certain cases; however, the model represented the overall
safety performance of a subject quite well. Test results showed clear evidence of the model’s ability
to capture safety conditions of workers in pre-identified hazard zones. The developed approach
presents a way to provide visualized and quantified information as a form of safety index, which
has not been available in the industry. In addition, such an automated method may present a
suitable safety monitoring method that can eliminate human deployment that is expensive, error-
prone, and time-consuming.
Keywords: construction; safety; awareness; communication; sensing
1. Introduction
Over the last decade, researchers have explored various technologies and methodologies to
enhance the safety of workers at construction sites. Regardless of the methods used, a holistic
approach to improving safety should be based on continuous monitoring of the construction site to
detect potentially unsafe conditions/hazardous events. However, the complex environment of indoor
construction sites and continuous changes in daily activities often lead to difficulty in conducting
safety inspections by site managers [1–4]. In addition, their method for conducting these inspections
relies on manual observation, which is inefficient, labor intensive, prone to error, inconsistent, and
costly [5–8].
Insufficiently identified safety issues may result in potentially hazardous events that may
escalate to injuries and fatal accidents. Even though the construction industry has adopted safety
training and regulations to enhance worker safety, safety issues have continued to threaten workers’
Sensors 2018, 18, 3897 2 of 18
health and lives, and have become a significant problem. Statistics from various organizations
indicate that the accident rate of the construction industry ranks among the highest among private
industries in the USA [9,10].
Researchers have explored using sensing technologies that can potentially benefit the
construction industry in various aspects [8,11,12]. For example, real-time location systems (RTLSs)
have been developed to monitor and collect real-time information from a site [13–19]. As of yet,
however, little research has been done to explore the issue of individual workers’ safety by using
RTLSs, and a holistic and integral approach has not been developed. To address this challenge, this
paper introduces a zone-based safety risk model that quantifies the safety performance of individual
workers based on a previously developed RTLS [20,21].
2. Background
Continuous monitoring of a construction site is crucial to provide workers with a work-friendly
environment that presents minimal hazards to their health and safety. In an effort to enhance safety, the
construction industry has adopted several methods, such as accident investigations, self-inspections,
surveys, and job hazard analyses. However, these are passive methods of data collection because they
require site observations or they are created after the undesired events already occurred; therefore,
all incidents that have the potential to lead to accidents that may not have necessarily been captured.
When monitoring and identifying safety-related occurrences, the construction industry has
relied heavily on manual efforts [22–24], such as data of past safety performance, which are recorded
manually after the occurrence of an event. These recorded data provide value in understanding the
issues and safety trends of the construction activities, but they require such steps as manual data
collection, aggregation, and postanalysis. Although such a method produces a project/company level
of safety information, which is still valuable, it is difficult to extract safety information for individual
workers from such a complex process.
For certain tasks, Occupational Safety and Health Administration (OSHA) requires the
designation of a competent person for safety purposes. This person should be able to identify existing
and predictable hazards at the site and should have the authority to take actions to eliminate such
hazards [25]. In recent years, monitoring of the safety conditions of workers has become more
challenging with the increasing complexity of construction projects. Because of this trend in
construction, safety managers are challenged with continuously monitoring and identifying incidents
that may cause safety problems, and their ability to accomplish this task and to make proper and
prompt decisions may be inadequate, in certain cases [26]. Furthermore, this limited capability of the
safety managers may cause some difficulty with regard to the need for ubiquitous and continuous
on-site monitoring for the precise identification of construction safety issues [27–29].
As a result, near-miss events—that is, incidents that could potentially escalate into an accident—
are often ignored or neglected by associated personnel, and are not properly recorded [30,31]. Li et al. [32]
pointed out that the number of near-miss incidents are considerably greater than the number of
accidents that are actually recorded. Previously explored methods [12,33] can only quantify safety
incident data in an on-and-off-based (or only alert based) metric, without having an ability to describe
quantitatively dangerous incidents that actually do not result in an accident. Unfortunately, all near-
miss events, which may escalate into accidents, could result in significant damage not only to the
associated person but also to the associated contractors. One recent study [12] developed a tracking
system to capture near-miss events, and Isaac and Edrei [33] advanced safety research forward by
presenting a statistical model as well as providing more proactive alerts for increased risk exposures.
These methods are advanced automated safety monitoring, and are effective in detecting safety
violations in a discrete manner. Despite the advancement made by past research, worker safety
performance is still difficult to understand in a simple, quantitative format. Therefore, for efforts in
promoting a safe construction environment, construction safety practices/research are still
inadequate, and the industry lacks an important measure for quantitative evaluation when handling
safety incidents. In addition, measures used in practice tend to be subjective, resulting in a different
conclusion each time, and from person to person. This study investigates a sensor-based safety
Sensors 2018, 18, 3897 3 of 18
monitoring approach in order to overcome this challenge by implementing a computational and
analytical procedure/model. Through the developed system, the continuous monitoring and
collection of a data stream from a construction site should be implemented so that detecting unsafe
conditions or hazardous events is possible. The developed framework or analysis procedure should
be available to process such data, using mathematical models to generate information that is
quantitatively meaningful. As a byproduct of these models, an objective safety evaluation will follow.
3. Objective and Scope
Despite available sensing approaches and concerns on worker safety, a large gap among sensor
data, modeling of safety issues, and individuals’ safety performance existed. This gap has not been
properly investigated, and unfortunately, individuals’ safety performance remains poorly
understood. Therefore, the objective of this research was to develop a sensor-based safety monitoring
method. This involved defining and developing parameters for zone-based safety risks, and
establishing a procedural model to quantify the zone-based safety risk (ZBSR) to individual workers.
The ZBSR model uses a tracking system based on Bluetooth Low Energy (BLE) that was developed
in previous studies [20,21]. The ZBSR model aims to mathematically process real-time location data
to produce measures that could assist in the understanding of the behavior of workers, which are
represented by safety performance indices that are computed based on locational information about
identified hazards and their associated parameters, such as the hazard boundary (e.g., core and
envelop zones) degree of exposure, frequency of exposure, and potential degree of injury. This
procedure serves as an objective, quantitative method to evaluate safety performance determined by
data collected onsite. To assess the module of the automated safety performance analysis, field
experimentations were conducted in a controlled setting.
The scope of this research (i.e., zone-based safety risks) included spatiotemporal hazards
predefined by zones and the workers’ interactions with these zones. According to the Health and
Safety Executive Annual Statistics Report [34], over 20% of fatal accidents in the construction industry
are associated with workers moving through zones at a construction site. Accidents also occur to
workers while they are executing their tasks in a nonhazard zone. However, because the direct causes
of such accidents vary, requiring a unique handling method for each cause, the scope of this study
was limited to zone-related hazards that can create dangerous situations to workers. These zone-
based hazards include, but are not limited to, hazards associated with the physical conditions of a
construction site—for example, unprotected large openings—which accounts for 38% of the incident
cases [35]. Such hazards are represented by their spatial and temporal relationship and the type of
construction activities, if any are nearby. The scope did not include other hazardous events that could
occur to workers while they are working at nonhazardous zones, such as cutting fingers, falling from
a ladder, mistakes when operating equipment, electrocution by mistake, and others.
4. Method
The approach using ZBSR for individual workers was developed by
1. Establishing hazard models,
2. Identifying the exposure relationship between workers and associated hazards,
3. Formulating a quantitative relationship between the associated hazards and modeling
parameters, and
4. Incorporating all of the parameters to compute an index that represents the safety performance
of the worker.
The following subsections describes these processes.
4.1. System Design
To develop a comprehensive safety monitoring system, this paper first introduces the system
design to establish the ZBSR model that uses real-time location data of workers onsite. Figure 1
displays a flowchart for the automated safety monitoring system, which integrates the tracking
Sensors 2018, 18, 3897 4 of 18
system with the ZBSR model. This integration allowed the evaluation of the safety performance of
individual workers based on their location data collected by the tracking system. As discussed
previously, only a little research has explored the use of RTLS in assessing the safety conditions of
individual workers. Therefore, the remaining subsections in methodology focused on the
development of a safety assessment approach that utilizes a real-time tracking system for quantifying
the safety performance of individual workers with respect to various parameters.
Figure 1. Flowchart of the automated safety monitoring system developed in this study.
4.2. Hazard Registration and Model
The safety performance of workers was assessed with respect to previously identified hazards
(which can be available by pre-project planning, daily site inspection, and training). Hazard
identification is typically carried out in two ways. By scrutinizing project information together with
building information modeling (BIM) and work schedules under hazard detection rules, certain
hazards could be identified automatically [7,36]. These types of hazards are often pre-identified
hazards as they can be found by analyzing associated project information. Unlike these types of
hazards, there also exist hazards that cannot be identified automatically. These types of hazards
usually reflect specific project or site information that could change over time as the work progresses.
Examples of these spatiotemporal hazards include poorly maintained areas, such as poor
housekeeping areas, inappropriately piled stock areas, broken barricades, and scaffolds that violate
safety rules.
Tracking System
Acquire
identified on-
site hazards
Identification
and modelling
of hazards
Zone
-
based
safety risk
analysis model
Zone-based Safety Risk Analysis Model
Location Data
Feed Location Data
Analysis
Safety Index
Hazard Areas
Sensors 2018, 18, 3897 5 of 18
Upon identification, hazards need to be modeled with certain parameters that quantitatively
define the hazards. Such parameterized hazard models allow the evaluation of the safety conditions
of workers with respect to the hazards. Each hazard varies with respect to type, size, and potential
consequences; therefore, modeling of these hazards needs to account for these factors.
To describe the hazards in a unique manner, the ZBSR model used a safety envelope approach
based on previous research [37–39]. Hazards that were defined using this approach provided
information regarding the core hazards and the hazard envelope with respect to certain geometric
information, such as radius, width, and length. The core hazard was represented by a zone that must
not be breached, and the hazard envelope was represented by a zone that should be protected. The
ZBSR model followed the same classification of hazards as found in a past paper [12]; any breach into
the core hazard zone was considered as an imminent hazard, and any breach into the hazard
envelope was considered as a caution event. All imminent hazards do not necessarily lead to an
accident, but they should be noted since they indicate a clear violation. This parameter can be
predefined based on hazard types and automatically parameterized in the system when the site
manager has identified site hazards. For example, one of the leading causes of occupational injuries
and fatalities are falls from portable ladders. As a means of protection, OSHA suggests erecting a
barricade around the ladder being used in order to keep traffic away from the ladder [40]. Such a
hazard was modeled by certain geometric shapes, such as a circle or an ellipse. Other types of hazards
were modeled by a rectangular shape, for example, large penetrations (i.e., large holes), storage areas
for hazardous material, restricted areas, and unsafe work zones.
Figure 2 shows examples of such hazards. The scaffolding hazard in Figure 2a is a type of hazard
that can be identified through onsite inspection, and the ladder hazard in Figure 2b is a type of hazard
that can be identified by project information analysis. The modeling of hazards to define geometric
information would be up to the user’s discretion (e.g., the safety manager or engineer). Depending
on the need for a detailed envelope zone, the user can set the geometric parameters of the envelope
zone from 0 to a specific range, the case of 0 being a hazard that was detected by on-and-off violations.
Figure 3 shows the parametric modeling of two types of hazards that have different geometric shapes
that eventually were fed into the ZBSR analysis model for safety performance assessment.
(a) Unsafe scaffolding without proper plates (b) Missing barricade
Figure 2. Hazards identified on a specific day for (a) a scaffolding hazard and (b) a ladder hazard.
Sensors 2018, 18, 3897 6 of 18
Figure 3. Parametric modeling of hazard severity.
4.3. Evaluation Metrics
Besides the parameters, which model existing hazards with respect to the discussed aspects, in
order to assess individual safety performance based on their location data by using hazard models,
the assessment also should account for dynamic location data in a solid relationship with the hazards.
The solid relationship should offer a guide for quantitative assessment of the safety conditions of the
worker by evaluating such parameters as the exposure level, exposure frequency, and the degree of
potential damage or injury upon occurrence of an accident. The quantification of such parameters
enables an objective assessment of the safety performance of workers in a systematic way.
The quantification of safety performance required concrete criteria and rules for assessing the
safety conditions of workers, based on given information. The first metric was the degree of danger
to a worker given the hazard models and the location data. It is evaluated based on the identified
hazard zones and the location data of workers tracked by a tracking system. For example, if the
worker was within the hazard envelope, it was considered a caution event, and the associated degree
of danger was computed based on the equation developed in the following sections. To further detail
out the quantification, a rule or a criterion using this geometric information of hazards was necessary
in order to evaluate the degree of danger to a worker when the worker was exposed to any of the
identified hazards. The rule needed to reflect the level of proximity in a relationship; this implies that
the closer the worker was to the core hazard, the greater the chances that the worker would be
involved in an accident.
Figure 4 displays a three-dimensional linear model for the degree of danger for a rectangular-
shaped hazard. Although a linear model was used in this research, the type of model is adaptable in
the proposed framework. Thus, depending on the safety manager’s discretion, or based on historical
data, the type of model of the degree of danger could be adjusted. This model assumed that any
breaches into the core zone was a critical event, whether it led to an accident or not; thus, all of such
breaches were noted with a ‘high impact’ score of 1. However, if the worker was found to be outside
of a hazard envelope, that situation was considered safe, and given a score of 0. The intermediate
zone between the core and envelope zones was measured by linear interpolation. The same rules
applied to other cases of hazard models. With this evaluation model, the uncertainty of the location
data was assessed quantitatively even further during the analysis.
Lenvelope
W
core
W
envelope
L
core
R
core
Renvelope
Legend
Core
Transition 1
Transition 2
Envelope
Sensors 2018, 18, 3897 7 of 18
Figure 4. Linear modeling in detail of the degree of danger of a particular hazard.
Each construction hazard presented various levels of danger; for example, ‘a-fall-to-a-lower-
level’ accident likely involves more serious damage to the workers affected than does ‘a-trip-
accident’. Despite this difference in potential damage, the method of introducing a safety envelope
may not sufficiently cover the consequences caused by its potential damage. To take this into account,
the ZBSR approach used a scaling factor to intensify the degree of danger. As this serves to estimate
the potential consequence to workers associated with the hazard, historical data is a good resource
to define the factor (e.g., if a-trip-accident is considered as a normal hazard having a scaling factor of
1, then a-fall-to-a-lower-level can be considered as a significantly dangerous hazard having a scaling
factor of 2). The procedure generated an index that indicated the rate of the occurrence of an accident
with respect to a specified time interval (e.g., per day). Because it is a representation of the degree of
danger at a certain time interval, and because the safety monitoring system continuously collects and
generates such data over a period, the system aggregated all of the data and produced a safety
performance index in various forms that depended on the inspector’s needs.
4.4. Zone-Based Safety Risk Analysis Based on RTLS Data
The ZBSR analysis occurred after the hazard modeling as well as the evaluation rules and criteria
were completed. This included incorporation with a feed of real-time location data from on-site
workers to complete the development of the quantitative procedure for the ZBSR analysis. In this
approach, contextual data (e.g., worker information and location information), which was collected
on-site, was translated into a quantitatively meaningful index that represented the safety condition
of individual workers. The translation factored in an understanding of the workers’ safety-related
behaviors and conditions. The associated parameters in the ZBSR analysis might not be quantified
deterministically because of the uncertainties involved; for example, assumptions when modeling of
parameters might lead to model uncertainties and quantified data might contain measurement
uncertainties. To account for variability and uncertainties, the ZBSR model used a probabilistic approach
to combine the parameters and input data in order to evaluate the safety performance of a worker.
Regarding a general overview of the equations associated to ZBSR, the quantification of safety
performance involved the various parameters discussed, expressed as:
Core
Envelope
Transition:
Interpolation
Degree of danger
Sensors 2018, 18, 3897 8 of 18
𝑠𝑝𝑖
,
=
𝑓
𝑙𝑜
𝑐
,
ℎ
𝑎
𝑧
,
𝑒𝑥
𝑝
,
𝑠𝑐𝑎𝑙
𝑒
,
𝑓𝑟𝑒𝑞
for
a
given
h
azard
and
location
,
(1)
where:
spii = safety performance index for given location, loci and the jth hazard
loc = location of position estimate
hazj = hazard models for the jth hazard
expi = exposure level/degree of danger for given location, loci
scalej = scale factor for the jth hazard
freq = frequency/exposure time
As the location estimation by the tracking system was not deterministic, the position estimation
was evaluated probabilistically with regard to its accuracy, based on the standard deviation of the
system as expressed as
𝑙𝑜
𝑐
=
𝑓
(
𝑥
,
𝑦
;
𝑥
,
𝑦
,
𝑠𝑡𝑑
)
(2)
where,
xi, yi = actually possible positions
xest, yest = position estimation from the system
std = standard deviation of the position estimation
The ZBSR analysis used a normal distribution for generating candidate particles (xi, yi), given
the location estimation (xest, yest) and the standard deviation.
Equation (1) was written for a given hazard, or the jth hazard. Two types of hazard models and
their associated parameters that describe the hazards are expressed as
ℎ𝑎𝑧=
𝑓
𝑙𝑒𝑛𝑔𝑡
ℎ
,
𝑤𝑖𝑑𝑡
ℎ
,
𝑙𝑒𝑛𝑔𝑡
ℎ
,
𝑤𝑖𝑑𝑡
ℎ
𝑓
(
𝑟𝑎𝑑𝑖𝑢
𝑠
,
𝑟𝑎𝑑𝑖𝑢
𝑠
)
for 𝑗th hazard (3)
For computing the exposure level, the necessary parameters are
𝑒𝑥𝑝
=
𝑒𝑥𝑝
=
𝑓
(
𝑥
,
𝑦
,
ℎ
𝑎𝑧
,
𝑠𝑐𝑎𝑙𝑒
)
=
𝑓
(
𝑝𝑟𝑜𝑥
1
,
𝑝𝑟𝑜𝑥
2
,
𝑠𝑐𝑎𝑙𝑒
)
(4)
where,
prox1 = the distance to the edge of the hazard core for a given hazard
prox2 = the distance to the edge of the hazard envelope for a given hazard
When computing the exposure level, the equation needs to be checked for all possible locations
for a given location with uncertainty, all identified hazards located nearby, and continuous time. For
simplicity, Equation (4) shows the computational formula for one hazard (i.e., no j term) and an
instant time (i.e., no k term). ZBSR first found the distances from the worker’s claimed location (xi, yi)
to the closest point of a hazard core (prox1) and that of a hazard envelope (prox2). It then used a linear
interpolation to quantify the degree of danger, also known as the exposure level.
Given a location datum point of a worker (i.e., the location estimation indicated by xest and yest)
at a specific time interval, the ZBSR model checked all of the nearby hazards to comprehensively
assess the safety performance. A general integral method that computes the safety performance index
with a given location estimation and its uncertainty was used, and is expressed as
𝑦
=
∫
∫
∫
𝑦
(
,
)
=
∫
∫
∫
𝑓
𝑙𝑜
𝑐
,
ℎ
𝑎
𝑧
,
𝑒𝑥
𝑝
,
,
𝑠𝑐𝑎𝑙
𝑒
,
𝑓𝑟𝑒𝑞
, (5)
where y is the safety performance index by ZBSR. Note that the frequency term has an additional
term k to account for the evaluation in continuous time:
𝑦
=
∑
𝑦
=
∑
∑
∑
𝑓
𝑙𝑜
𝑐
,
ℎ
𝑎
𝑧
,
𝑒𝑥𝑝
,
,
𝑠𝑐𝑎𝑙
𝑒
,
𝑓𝑟𝑒𝑞
, (6)
where j is the index for hazards and k is the index for time.
Sensors 2018, 18, 3897 9 of 18
However, the integral in the safety performance equation is a continuous function over time,
defined by the k term. Because of the complexity in solving this continuous integral with discrete
data, Equation (5) instead was modified to a numerical summation so that the assessment was made
in a discretized manner, as shown in Equation (6). In the discretized version of the assessment, index
j covered the situation where the worker was involved with more than one hazard, and index k
aggregated the safety performance evaluations that were continuously generated as the worker
continued movements. The system yielded the corresponding safety evaluations.
5. Experiment and Result
The experimental test involved two sets of field experimentations to test the ZBSR models by
quantifying the safety performance of a worker who was exposed often to hazardous areas. For safety
reasons, this test was conducted in a controlled environment with trained subjects, and emulated
certain safety incidents and violations that could control the safety conditions of the site. A controlled
movement, which served as ground truth, provided a benchmark for comparison with the
performance results acquired by the proposed approach.
Figure 5 shows the two test beds and the associated hazard areas. This validation assumed a
locational accuracy of approximately 1.5 m, which was concluded from the author’s previous studies
with a BLE-based location tracking system [20,21]. As used in past work [12], BLE sensors were laid
out over the site with an interval of 5 m. This system offered a sampling rate of 0.7 data per second.
The tracking data were collected and analyzed with respect to the pre-identified hazards. This
accuracy was used as the uncertainty input when processing the ZBSR model for quantifying the
safety performance of a test subject. Detailed information associated with the tracking system can be
found in the authors’ previous work [20,21]. The framework developed in this research used the
accuracy of a tracking system as an input to the safety evaluation system, and should work for any
tracking system in the same manner.
To create various cases that represent a range of degrees of the level of proximity, exposure time,
and exposure frequency, the study designed a multitude of scenarios for each of the two testbeds.
Figure 6 shows the scenarios, which were designed such that the projected positions were located in
various locations (core, transition, and envelope) within a hazard. The size of each imminent hazard
zone was specified by the site manager, based on the space and conditions of the hazard. The size of
the corresponding caution was chosen to be twice as large as that of the imminent hazard [12] that
the safety manager considered reasonable. Based on these scenarios, the subject passed through a
hazard zone and/or stayed in/out of a hazard zone. The tracking system collected the location
information of the subject. Then, the ZBSR model was applied to interpret and analyze the data in
order to assess the safety performance of the subject in the form of a safety index.
Testbed 1: outdoor site
Hazard in testbed 1
Sensors 2018, 18, 3897 10 of 18
Figure 5. Two testbeds and their hazardous areas.
(a)
(b)
Figure 6. Tested scenarios in two test sites: (a) ground truth data and (b) tracking results.
Figure 7 shows two samples (i.e., Scenario 2 in Testbed 1 and Scenario 2 in Testbed 2) of tracking
results and corresponding ground truths; the arrows indicate the direction of movement in the paths.
Note that in actual application, the system does not know ground truth, but has to rely solely on the
tracking data, which justifies the adoption of a probabilistic approach in our framework. Once the
tracking data were collected, the system fed them into the ZBSR model for analysis. Because of the
uncertainties as discussed previously, the safety performance was assessed probabilistically by
Testbed 2: indoor site Hazard in testbed 2
Sensors 2018, 18, 3897 11 of 18
applying Equations (1)–(6). ZBSR first received the streaming of the position estimation—that is, it
takes each of the estimated points individually into the analysis—and associated the estimation with
the hazard models registered in the system. After processing the position data by using Equations (1)–(6),
the probabilistically assessed safety performance is generated.
Figure 7. Zoomed-in view of tracking results and the corresponding ground truths of certain test cases.
The authors selected three sample points for illustration purposes (see Figure 8), these three points
were selected from Scenario 2 in Testbed 2 that best describe the situations. The first point, shown in
Figure 8a, represents the case of safety assessment when the subject is near the hazard zone but does
not invade the zone. The second point, shown in Figure 8b, represents the case of safety assessment
when the subject is in the hazard core. The third point, shown in Figure 8c, represents the case of safety
assessment when the subject is inside the hazard envelope but outside the hazard core. As observed,
the number of points in each of the hazard zones seems reasonable because the decreasing number of
+ marked points from the case in Figure 8a–c properly reflects the increasing exposure level, and the
numbers of + marked points are 79%, 40%, and 13%, respectively. The right-hand plots for each case
show a 3D evaluation of the hazardous degree of each of the (x, y) points, based on the linear model.
The plots yielded congruent results with previous observations. As the position estimation advanced
towards the core of the hazard zone, the number of points on a high scale of increase.
(a) Probabilistic evaluation of a point outside the hazard zone
Scenario 2 Testbed 1 Scenario 2 Testbed 2
79 % is marked as +.
(3.28, 0.37)
Sensors 2018, 18, 3897 12 of 18
(b) Probabilistic evaluation of a point in the transition zone
(c) Probabilistic evaluation of a point inside the hazard core
Figure 8. Evaluation of sample points from Scenario 2 of Testbed 2.
In order to scrutinize the degrees of danger, the right-hand 3D plots in Figure 8 were converted
to 2D plots, as shown in Figure 9. Figure 9a, which represents the case of ‘the point outside the hazard
zone’, contains sporadic data points that are greater than a hazard index of 0, while having a large
portion of points equal to a hazard index of 0. This trend in the hazard index increases as the subject
moves towards the hazard area, as shown in the cases of ‘the point inside the hazard envelope but
outside the hazard core (Figure 9b) and ‘the point inside the hazard core’ (Figure 9b). When the
subject was estimated to be in the hazard core, the corresponding plot contained a large portion of
points that were equal to or greater than a hazard index of 0.5.
(a)
40 % is marked as +.
(5.76, 2.73)
13 % is marked as +.
(2.83, 2.77)
79 % is equal to 0.
8% is greater than
0.5.
Sensors 2018, 18, 3897 13 of 18
(b)
(c)
Figure 9. Detailed assessment of each of the evaluations. (a) 2D evaluation of a point (3.28, 0.37)
outside the hazard zone (corresponding to Figure 8a); (b) 2D Evaluation of a point (5.76, 2.73) in the
transition zone (corresponding to Figure 8b); (c) 2D Evaluation of a point (2.83, 2.77) inside the hazard
zone (corresponding to Figure 8c).
In sum, the graphs indicate that higher scores were observed more frequently as the subject
moved from outside of the hazard zone to inside the hazard envelope and from inside the hazard
envelope to the hazard core. It is important to note that the analysis was based on a probabilistic
estimation of the subject’s location, and it inherently contained probabilistic errors (as the standard
deviation of the tracking system was used for error quantification). For the case described in Figure 9a,
this probabilistic error produced 8% of the estimations to have discrete hazard indices higher than
0.5. This was reasonable because the position estimation (3.28, 0.37) was close to the transition
boundary in which a small error could result in a non-zero hazard index. For the other cases described
in Figure 9b,c, similar observations regarding the effect of the probabilistic assessment were found.
So far, this paper has introduced the assessment of each point based on the developed models
and equations. Because each of these (x, y) points were assessed probabilistically, each had its own
likelihood of occurrence at a rate of one per the number of data points, which in this case was 1/3000.
After taking into account this likelihood for each datum point, the safety index was computed as
shown in Equation (6). Note that the description made in this context focused on the three points
selected; however, the safety evaluation system processed all of the estimated points—in this
example, the estimated points were the points illustrated in Figure 7—and yielded the safety
performance indices assessed for the points.
Figure 10 presents the results of the ZBSR analysis for Scenario 2 of Testbed 2 on the safety
performance index, which well-represented the safety conditions of the test subject in a probabilistic
manner. Scrutiny of the data in Figure 10 revealed the following.
The subject was in the safe area for about 10 s.
From approximately 10 s, the subject was exposed to a low hazard level.
From approximately 17 s, the hazard level increased sharply (spiked).
The subject was detected as staying in the hazard zone from approximately 20 s to 25 s.
From approximately 25 s, the hazard level dropped to almost zero at approximately 28 s.
The subject was detected again in the hazardous zone, and the hazard level spikes up from
approximately 35 s.
The subject was detected to stay in the hazard from approximately 36 s to 40 s.
40 % is equal to 0.
20% is greater than
0.5.
13 % is equal to 0.
71% is greater than
0.5.
Sensors 2018, 18, 3897 14 of 18
From approximately 40 s, the hazard level dropped, and the safety hazard condition
disappeared.
These summaries represent the actual movement reasonably well (i.e., ground truth) of the
subject simulated in Scenario 2 in Testbed 2. When generating the safety performance index (SPI),
these details were compiled into one single safety index as described in Equation (6). This
quantification was important, and varied from the conventional method. The results not only
described the behavioral phenomena of the subject but also quantified the safety performance of the
subject, based on the given hazards and their associated modeling information.
Figure 10. Safety performance index for Scenario 2 of Testbed 2.
To compare the safety performance index (SPI) resulting from the test data with that resulting
from the ground truth, the same analysis was conducted on the ground truth data set. Figure 11 plots
the SPI in a compact scale for each of the two cases, the test case and the ground truth case. Overall,
the SPI of the test case seemed capable of reflecting the safety conditions of the subject as it
represented relatively well the SPI trend. One of the intriguing findings from observation of the
results was that the SPI of the test data underestimated the safety evaluation of the subject when
compared to SPI based on ground truth data. Although this was not a desirable observation, it was
inevitable because of the uncertainty of the associated parameters. The difference between the two
SPIs could be partially explained by the sample data from Figure 8. Figure 8c shows scattered data
points that are extracted from a given position data set. These scattered points indicate possible
locations with unique weights assigned to them. In this case, because it was a probabilistic approach
with the scattered points, the procedure not only used points within the core but also used points in
the envelope area as well as in the safe area. However, in the case when using the ground truth,
because it was a deterministic measure, the SPI was computed to be 1.0. As a result, the lower SPI by
the system was inevitable: the SPI from the test data set was 0.251 critical safety incident per day for
the test time period, and that from the ground truth was 0.330 critical safety incident per day.
Another important observation, from analyzing certain segments in Figure 11, was that when
the subject was near the boundary of a core zone (for times 20–25 s and 35–40 s), the automated safety
evaluation approach, as compared to ground truths, underestimated the SPI. When the subject was
near the boundary of an envelope zone (for times 10–15 s and 28–33 s), the automated approach
overestimated the SPI. This observation was reasonable because the study used a tracking system
that offered 1.5-m accuracy, which resulted in probabilistic errors. By using a more accurate tracking
system or a dense system network, this issue of underestimation or overestimation could be reduced.
Case a)
Case c)
Case b)
Case a): outside the hazard
zone
Case b): inside the hazard
envelope but outside the
hazard core
Case c): inside the hazard
core
Sensors 2018, 18, 3897 15 of 18
Figure 11. Comparison of safety performance indices for test data and ground truth.
Table 1 compares results from the test data and the corresponding ground truth data. Certain
cases—including Scenarios 1 and 4 in Testbed 1 and Scenario 3 in Testbed 3—that simulate a safe
situation but are located near the hazard envelope, led to a fairly accurate estimation of the safety
condition as they produced errors of 0.0024 and 0.009 critical safety incident per day. This was
because the differences between the SPIs of the ground truths and that of the test data sets were small.
Other cases in which certain unsafe movements were observed resulted in similar findings, as
discussed above. Note that the SPI values were based on the duration of the data collection, so the
SPI values continuously changed with the data feed in the analysis (Figures 10 and 11). For most
cases, aggregated SPI values of the test data were lower than those for ground truth (Figure 11). Based
on previous observations (i.e., underestimation near the core, which was discussed in detail with
Figure 8c), this was expected because the test cases involved interactions with the hazard core, which
was the case when underestimation was observed.
Table 1. Comparison of the Aggregated Safety Performance Index for ground truth and test data.
Testbed Scenario SPI of Ground Truth SPI of Test Data
1
1 0 0.0024
2 0.328 0.221
3 0.253 0.295
4 0 0.028
5 0.619 0.506
2
1 0.390 0.291
2 0.330 0.251
3 0 0.009
Ground Truth
Test Data
Sensors 2018, 18, 3897 16 of 18
6. Conclusions
The construction industry has been suffering from inefficient methods of quantifying safety-
related hazards with limited resources. To overcome this challenge, this study developed a sensor-
based method by establishing a framework for an automated safety monitoring system, and
presented a new analytical and computational method to evaluate the safety performance of workers
by using a ZBSR model. To assess the performance of the developed model, two sets of experimental
studies were conducted at construction sites.
The experimental studies assessed the ability of the ZBSR model for quantifying the safety
performance index for workers with respect to pre-identified and registered hazards. Various
hazards could be defined and updated differently based on their work duration; however, this was
out of the scope of this study, and details can be found in a previous paper [12]. The various test
scenarios and setups simulated diverse conditions, which varied the conditions of the parameters
that affected the quantification of the safety index. For scenarios 1, 3, and 4 in testbed 1, and scenario
3 in testbed 3, errors were small, as none exceeded a 5% (0.045). Because of the nature of probability,
the probabilistic evaluations showed slight underestimations for scenarios 2 and 5 in testbed 1, and
scenarios 1 and 2 in testbed 2, with errors of 0.107, 0.113, 0.099, and 0.079 critical safety incidents per
day, respectively. However, they represented the overall safety performance of a subject well, and
will be improved if more accurate tracking is achieved. The test results showed clear evidence of the
model’s capability in capturing the safety conditions of workers with respect to nearby hazards,
based on location data from the tracking system. Such a capability to quantify the safety performance
of workers provides unprecedented levels of information to the project/site manager. This
information can be useful for daily safety trainings as well as for real-time warnings to reduce site risks.
The approach is advantageous over conventional methods because it can offer an impartial,
automatic (or semi-automatic), and continuous job safety analysis, as well as a job-safety plan, thus
eliminating problems related to workers’ safety stemming from a lack of understanding of the safety
performance or the behavior of individual workers. Despite these advantages, this method is not yet
to replace the current practice of safety inspections because the current safety site inspections and the
proposed method of safety analysis address different aspects of safety concerns.
Although methodological and procedural developments were conducted in the research, the
approach has a few limitations, which may be investigated in future research. First, the study relied
on a tracking system that has a known accuracy level that was used as an input to the ZBSR analysis
model. Second, the ZBSR model was limited to handling certain types of hazards that were defined
by using geometric information for quantification purposes. Third, for more precise quantification of
safety performance, the customization of workers in a parametric manner may be needed to account
for skilled workers, as well as workers who are fully aware of a specific hazard and need to operate
nearby the hazard. Fourth, because of the scope defined in the research, the evaluation of the safety
performance index was not a reflection of safety evaluations for all types of safety issues on site, but
was limited to those related to hazard zones. Last, but foremost, the purpose of ZBSR was to capture
near-miss events and to quantify their risk levels in order to better understand the potential risks to
workers when they are on site. However, most of the tested site data were based on simulated
scenarios to validate the proposed theoretical approach. Thus, a case study with trade workers at a
construction project would be necessary for a real-world validation. Furthermore, future study can
explore each of the parameters in detail to refine their mathematical models and add additional
parameters to add site and person dependent characteristics.
Author Contributions: This work is based on J.P.’s Ph.D. thesis work supervised by Y.K.C. A.K. contributed to
data analysis and manuscript development.
Funding: This research received no external funding.
Acknowledgment: The publication fees for this article were supported by the University of Nevada, Las Vegas
(UNLV) University Libraries Open Article Fund.
Conflicts of Interest: The authors declare no conflicts of interest.
Sensors 2018, 18, 3897 17 of 18
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