AN INFERENCE METHOD OF SAFETY ACCIDENTS OF … · Accidents of Construction Workers According to the Risk Factor Reduction of the Bayesian Network Model in Linear Scheduling, International

http://www.iaeme.com/IJM/index.asp 1 [email protected]

International Journal of Management (IJM) Volume 11, Issue 7, July 2020, pp. 1-12, Article ID: IJM_11_07_001

Available online at http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=7

ISSN Print: 0976-6502 and ISSN Online: 0976-6510

DOI: 10.34218/IJM.11.7.2020.001

© IAEME Publication Scopus Indexed

AN INFERENCE METHOD OF SAFETY

ACCIDENTS OF CONSTRUCTION WORKERS

ACCORDING TO THE RISK FACTOR

REDUCTION OF THE BAYESIAN NETWORK

MODEL IN LINEAR SCHEDULING

Jongsik Lee

Professor, Department of Architectural Engineering,

Songwon University, Gwangju Metropolitan City, Republic of Korea.

ORCID: 0000-0001-6372-3554

Jaeho Cho*

Research Professor, Department of Architectural Engineering,

Dankook University, Gyeonggi-do, Republic of Korea.

*Corresponding Author

ABSTRACT

To protect workers' safety and health at the construction site is becoming a core

value of the construction site. A safer working environment is created when risk

factors on site work are recognized in advance. This study targets the worker's 1-day

cycle activity in Linear Scheduling. In addition, this study suggests a Bayesian

network model that presents risk factors and probability of accidents. Accidents come

down to a final disaster through causal relationship from various existing risk factors.

The physical risk factors of the workspace are the most direct cause of accidents. This

study uses the Bayesian network model to compare the probability of accidents caused

by changes in the safety environment of the work from the risk factors that have been

eliminated and those that have not been eliminated. Statistical probability values

confirm that accident risks can be remarkably reduced when all possible risk factors

are reviewed and eliminated in advance.

Key words: Building Construction, Safety Management, Linear Scheduling, Bayesian

Network

Cite this Article: Jongsik Lee and Jaeho Cho, An Inference Method of Safety

Accidents of Construction Workers According to the Risk Factor Reduction of the

Bayesian Network Model in Linear Scheduling, International Journal of

Management, 11(7), 2020, pp. 1-12.

http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=7

An Inference Method of Safety Accidents of Construction Workers According to the Risk Factor

Reduction of the Bayesian Network Model in Linear Scheduling


1. INTRODUCTION

1.1. Background and Purpose of Study

According to the construction accident statistics in 2018 of the Korea Industrial Safety

Association, the number of deaths in the construction industry was 579, an increase of 4.5%

compared to the previous year. The main causes of safety accidents are unstable working

environment at the construction site, unskilled workers' behavior, excessive production

pressure, improper allocation of resources and time and insufficient training. For safe work,

prior recognition of risks must be strengthened. Also, a safe environment should be created by

minimizing risk factors. The first step for safety is to conduct a thorough check according to

the safety review list. Recently, studies have been actively conducted to evaluate and

recognize risks in advance that may occur in the construction work process using BIM-4D

(3D-Scheduling) information [1-7]. BIM-4D (3D-Scheduling) information provides the

worker with a visualized field work process. This provides a safer work environment by

helping workers understand the work and easily identify potential risks. Risk factors at the

site must be recognized and eliminated or minimized prior to commencement of work. Recent

safety management studies automatically provide safety checklists to workers by informing

the causes of accident risks. In addition, these studies are being actively carried out around IT

technology that can recognize the risks of the workspace in advance using Internet of Things

(IoT) technology [8]. Future construction technologies are expected to develop in the

direction of BIM-Virtual Reality (VR) where workers experience risks and identify risk

factors [9-10]. Another approach in safety management study is AI learning. In developed

countries regarding to the safety management, including the United States and Europe,

learning-based safety accident inference methods using the fuzzy theory and Bayesian

network have been continuously studied [11-13].

This study targets the planned activities of workers. In addition, this study proposes an

accident risk inference method using the Bayesian network, based on Linear Scheduling

Method that can easily apply BIM-4D technology. Here, the inference of accident risk

compares the probability of an accident when the risk factor inherent in the work is eliminated

and the probability of an accident when the risk factor inherent in the task is not eliminated.

This study does not focus on 'Whose fault is the accident?' and it has started with the purpose

of preventing accident recurrence by analyzing the root causes (the process of causing causes

over time) regarding to „Why is the accident repeated?‟ and „What is the problem?‟. Many

researchers have proposed various methods about accident cause analysis. However, studies

that predict accidents with similar risk factor information and reduce the accident risks have

been lacking. On-site feedback information providing real-time accident causes which can be

occurred in a similar work environment is one of the effective ways to minimize accident

occurrence. In another way, the experience and knowledge of the safety manager is an

important factor, so building a knowledge data platform for the accident cause is the most

powerful tool that can be shared the safety knowledge of numerous works of construction. In

order to reduce safety accidents, an implementation plan of construction work must be

premised. If the on-site scheduling and schedule progress do not coincide and are proceeding

differently, the risk of an accident will inevitably increase due to an inadequate safety plan. If

the planned safety plan differs from the actual safety plan, the risk of accidents increases in

the schedule management on site. Therefore, the risk assessment of the construction site

should be evaluated according to the actual implementation scheduling.

BIM-4D technology is the basis for on-site schedule synchronization. Once an approved

implementation plan has been prepared for the worker's 1-week work and 1-day work, the

site's safety evaluation (safety check) can predict the risk based on the implementation work

Jongsik Lee and Jaeho Cho


plan. If the work is delayed or changed, the site safety evaluation (safety check) is re-executed

based on the changed work. The reliability of the risk assessment of the work can be secured

based on the worker's changed schedule and working environment.

2. METHOD OF STUDY

Several construction accident prediction studies have proposed accident prediction algorithms

using Case Based Reasoning method or AI neural network. These studies, however, do not

sufficiently prove the reliability and effectiveness of the construction accident prediction [14].

Current safety accident prediction methods extract accident data from similar work types and

infer the risk of accidents for the current planned work. To secure the reliability of the

accident prediction, the worker's work location and the surrounding environment of the work

are important factors. Therefore, in order to secure the effectiveness of the prediction

algorithm, 1-day actual plan must be reflected. Among various scheduling methods, Linear

Scheduling Method is the most effective schedule management method that can track and

manage the work location of workers in real time.

This study targets formwork and proposes an inference method in the following order to

predict the accident risk. First, the root causes of the work are analyzed through accident data

of formwork. Second, the risk factors of the formwork are classified by the Fault Tree

Analysis (Hereinafter, FTA) frame. Third, the Bayesian network model is applied to Linear

Scheduling in order to infer workers' risks. Fourth, the risk probability of accidents is

compared when the risk factors are identified and removed in the formwork and when the risk

factors are not eliminated. The results of the study are summarized and the limitations of this

study and future study directions are suggested.

3. STUDY MODEL DESIGN

3.1. Location-based Scheduling in Linear Scheduling

Linear Scheduling Method (Hereinafter, LSM) is a graphical scheduling method focusing on

continuous resource utilization in repetitive activities [15]. LSM is used mainly in the

construction industry to schedule resources in repetitive activities commonly found in

highway, pipeline, high-rise building and rail construction projects. These projects are called

repetitive or linear projects. LSM is called by various names such as VPM (Vertical

Production Method), RPM (Repetitive Project Model) and Location Scheduling. LSM is

divided into units of work or work location and work zoning on the vertical axis, and the

horizontal axis is expressed as the flow of time. The slope of Linear Scheduling indicates the

productivity or construction speed of the work. LSM is relatively easy to write than network

scheduling and provides more information than Bar Chart. In addition, LSM has the

advantage of being able to simply plot the schedule progress rate for each workspace of the

work which is difficult to express in a bar chart or network scheduling.

In general, one work is represented by a parallelogram (Vertex A-B-C-D) with the slope

). The X-axis is the time axis and the Y-axis is the units of a unique workspace or facility.

In the case of Figure 1, there are workspaces from 1 to N. It indicates that the workspaces are

constructed sequentially from the lower numbered workspace to the higher numbered

workspace. The points A and B of the parallelogram indicate the time at which the work was

started and completed by putting 'Work Team 1' into 'Zone 1', respectively. The points C and

D of the parallelogram indicate the time at which the work was started and completed by

putting „Work Team 2‟ into „Zone N‟, respectively. The distance between points A and B of

the parallelogram means the period (work time of Zone 1) in which „Work Team 1' was put

into 'Zone 1' to perform the schedule. The distance in the X-axis direction between points A

and D represents the total duration of the work performed by the entire project zones. Linear




scheduling is a method to achieve the optimization of the entire project by adjusting the

working time of the workspace, the number of work groups and the slope ) of the work.

Line of Balance (Hereinafter, LOB) is a management control process used in construction

Figure 1 Expression of Linear Scheduling Method

Figure 1 is the concept of Linear Scheduling that X work teams are put and consisting of

N zones which one work is procured sequentially [15].

The purpose of using LOB in Linear Scheduling is to determine the appropriate number of

workers to be put into each repetitive task to support balanced schedule management. LOB

shows the process, status of project, work size continuity, and background of work, time and

phase of project activities providing management with measuring tools. LOB assists project

management by comparing a formal objective against actual progress, examining only the

deviations from established plans, and gauging their degree of severity with respect to the

remainder of the project, dealing with problem and trouble causing areas and problem solving

within specific constrains.

LOB is performed in the following order as shown below.

(1) Unit of the unit is constructed at a ratio that satisfies a predeterminate deadline.

(2) Maintain a logical Critical Path Method (CPM) network of each unit.

(3) Work continuity of the worker is maintained.

(4) Measure the degree of work progress in the workspace or unit.

(5) Maintain the balance of the work by analyzing the severity of the problem with respect to

the remaining work units.

Figure 2 Example of Linear Scheduling



Figures 2 and 3 are examples of LOB analysis expressing the degree of work progress by

workspace with the base of 18 days of work progress in Linear Scheduling.

Figure 3 Example of LOB analysis that analyze the degree of progress by unit

3.2. Risk Factor Analysis of Worker

FTA is frequently used in study and practice to discover the root cause of accidents and it was

developed in 1962 by Waston of Bell Labs, USA. FTA is a deductive method for identifying

the cause of a specific accident and it is a safety evaluation method that is widely used for

quantitative prediction of accidents. FTA draws fault tree diagrams that represent various

combinations of device disorder and failures that can lead to accidents or events. First, it is

assumed that accidents do not occur if the work proceeds as usual in order to make FTA. It is

analyzed the causes over time in order, with the progress of the work as the Y-axis. To

finalize FTA, the indirect causes are eliminated and only the direct causes are subdivided and

materialized. The most direct causes are classified at the lowest stage of FTA and abstract

them at the upper stage to summarize them as conceptual causes.

The following figure 4 is the concept converting the cause of accident over time into FTA.

Figure 4 FTA converting concept of accident cause over time

3.3. Bayesian Network

A Bayesian network, Bayes network, belief network, decision network, Bayesian model or

probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of

statistical model) that represents a set of variables and their conditional dependencies via a

directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred

and predicting the likelihood that any one of several possible known causes was the




contributing factor. For example, a Bayesian network could represent the probabilistic

relationships between diseases and symptoms. Given symptoms, the network can be used to

compute the probabilities of the presence of various diseases. In addition, in the Bayesian

network, it is possible to apply effective algorithms for performing inference and learning.

Using the Bayesian network, a causal relationship of construction accident from the risk

factors regarding to the construction work is set and the accidents of the construction workers

can be predicted through a prediction algorithm. Safety evaluation of construction work is

replaced by the concept of symptoms, and construction accidents are replaced by the concept

of diseases.

The most basic Bayesian network equation is the following.

Several equivalent definitions of a Bayesian network have been offered. For the

following, let be a directed acyclic graph(DAG) and let be a set

of random variables indexed by . is a Bayesian network with respect to if its joint

probability density function (with respect to a product measure) can be written as a product of

the individual density functions, conditional on their parent variables [16].

∏

where is the set of parents of (i.e. those vertices pointing directly to v via a single

edge). For any set of random variables, the probability of any member of a joint distribution

can be calculated from conditional probabilities using the chain rule (given a topological

ordering of ) as follows.

∏

Using the definition above, this can be written as:

∏

The difference between the two expressions is the conditional independence of the

variables from any of their non-descendants, given the values of their parent variables.

4. STUDY MODEL

4.1. Bayesian Network of Work Risk

The basic structure of the work risk using the Bayesian network is divided into the causes of

the lower stage and the causes of the higher stage. The basic structure of the Bayesian

network for accident prediction in construction work was proposed in a study by Limao

Zhang [14]. The parent node at the top stage represents an accident occurring in construction.

In the parent node, an accident in the construction has the child node which is the second

stage. The occurrence of the cause of the child node sequentially leads to the result of the

parent node. The parent node has multiple child nodes of the lower stage and the parent node

of the higher stage is defined as an abstract cause. The child node of the lower stage is defined

as causes of practical physical accidents.

The topic of this study began with the premise that the risk factors identified in

construction work can be eliminated or reduced. Theoretically, the causes of the physical

accidents identified by a safety manager can be eliminated to prevent accidents at the

construction site. However, unidentified risk factors or potential risk factors that have been



identified but not eliminated remain. The Bayesian network can increase the accuracy of

inference by removing only the confirmed cause of the accident.

The following Figure 5-(a) is the basic structure of the Bayesian network that analyzes the

probability of an accident. The following Figure 5-(b) shows the Bayesian network inference

concept that infers the accident occurrence by removing the risk factors that are not likely to

occur through confirmation by the safety manager.

(a) Basic structure of Bayesian network to interpret the accident occurrence stochastically

(b) Bayesian network inference concept inferring the accident occurrence

Figure 5 Basic model of Bayesian network for inferring the construction accident and the accident

causes which are eliminated through the safety review

4.2. Linear Scheduling and Integrated Model of Risk Factors

The advantage of the Linear Scheduling is that the completed work and the scheduled work

are clearly divided. Based on the Linear Scheduling, the daily schedule progress check and

the implementation plan of the tomorrow's work are prepared so that risk factors can be

identified before the work is performed. The risk factors of the work must be based on the

location of the facility. Therefore, the risk assessment of the work can be integrated through a

daily scheduling and a spatial classification system of facilities.




The following Figure 6 shows a model that integrates schedule management and safety

management on a daily basis.

Figure 6 Integrated model of scheduling and safety

5. CASE APPLICATION

Among the construction works, formwork is a work that causes more accidents than other

types of construction. According to a report that analyzed the current status of accidents in the

Korean construction industry in 2018 by schedule, 4,529 of the 18,891 injured workers were

formwork workers, accounting for 24% of the total. The incidence rate by type of accident

was analyzed as falling (30.4%), hit by objects (20.8%), slipping (19.1%), bumping (13.1%)

and pinching (8.3%) [17].

Table 1 categorizes the physical factors of serious accidents occurred during the

formwork. Representative physical factors can be classified into improper working method,

insufficient safety equipment and insufficient electrical equipment, and detailed risk factors

have been classified.

Table 1 Cause of serious accident occurrence of formwork

Item Main risk cause Number of

occurrences

Component

ratio

Improper

working

method

Poor assembly work and installation conditions of

formwork, copper and temporary equipment

17 53%

Poor material loading method 2

Poor work path 32

Non-compliance with safety pitch 4

Stacking soil on top of the excavation 1

Stylobate disorder of site 1

Non-compliance with construction process 2

Insufficient

safety

equipment

Fall prevention non-installed and insufficient 32 44%

Insufficient fall prevention net 4

Poor horizontal coupling installation 1

Without chain block hook release device 1

Insufficient safety belt 9

Insufficient retaining of earth reinforcement 2

Insufficient

electrical

equipment

Electric machine short circuit 4 4%



Using the classification results for the risk causes of formwork accidents in Table 1, the

FTA model for the occurrence of accidents in formwork can be prepared as shown in Figure

7.

Figure 7 FTA analysis model for formwork

The following example of a linear scheduling is a two-story concrete frame construction.

The construction area is divided into three areas: A, B and C, and the work carry out in each

area. The work duration and the number of workers of each work are shown in Table 2 below.

The base point and location for the prediction of accidents is Area A on the n+1 floor of Work

2 Formwork on Day 11.

Table 2 Work duration and the number of workers of each work

Number of work Work type Work duration (day) Work group

Work 1 Rebar construction 2 days Work group 1

(6 workers)

Work 2 Formwork 2~3 days Work group 2

(9 workers)

Work 3 Concrete pouring 1 day Work group 3

(5 workers)

Figure 8 Linear Scheduling of concrete construction




The following presents the inference process of the formwork accident by combining the

FTA model of the formwork and the Linear Scheduling which are prepared. The formwork's

FTA model is converted to the Bayesian network model as shown in Figure 9. The scope of

the accident inference is limited to the causal relationship of X1, X2, X3, X4(1) and X5. X1 is

improper working method, X2 is insufficient safety equipment, X3 is caused by insufficient

electrical equipment, and X4(1) is the risk factor of X1, X2 and X3, indicating the slipping of

the worker.

For workers, the probability of occurrence at each node was assumed as follows through

data collection of accident cases.

(1) P(X1): Improper work method (total probability 5% to be performed as inappropriate work

for all workers in construction work)

(2) P(X2): Insufficient safety equipment (4% probability of insufficient safety equipment for

all workers in construction work)

(3) P(X3): Insufficient electrical equipment (0.4% probability of insufficient electrical

equipment for all workers in construction work)

(4) P(X4(1)): Slipping, pouring out (0.2%+0.05%=0.25% probability of slipping accident for

all workers in construction work)

(5) P(X4(2)): Falling (0.33% probability of fall accident for all workers in construction work)

(6) P(X5): Serious accident regarding to formwork (0.6% probability for the entire

construction work)

The induction probability of X4(1) by X1, X2, X3 is set as follows.

Therefore, the combination probability of P(X1,X2,X4(1)) is described as the following

equation.

( | )

If the risk factor of X3 exists, the combination probability of P(X1,X2,X3,X4(1)) is

calculated as follows.

( | ) | )



It can be seen that the probability of P(X1,X2,X3,X4(1)) is increased by 1.44% than the

probability of P(X1,X2,X4(1)).

Figure 9 Bayesian network model that can occur in formwork

6. CONCLUSION

Risk assessment and safety check performed daily at construction sites are the most important

elements of accident prevention. This study proposed a Bayesian network inference method

for the accident risks of the worker in construction work based on the Linear Scheduling. The

cause of the accident was analyzed over time to analyze the root cause of the accident. If this

study model is used, it is expected that preemptive safety management having secured

reliability will be possible by evaluating the risk factors in response to individual tasks of

workers.

The risk probability of formwork in this study was inferred using the assumed data.

Therefore, it is considered that it is necessary to collect and analyze macroscopic big data

about the cause of the accident in order to increase the accuracy of the risk probability of this

study model. In addition, in order to effectively reduce safety accidents, it is necessary to

collect and analyze data for the entire construction work.

ACKNOWLEDGEMENTS

This study was supported by research fund from Songwon University 2020 (C2020-05).

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AN INFERENCE METHOD OF SAFETY ACCIDENTS OF … · Accidents of Construction Workers According to the Risk Factor Reduction of the Bayesian Network Model in Linear Scheduling, International