AN ANALYSIS OF ACCIDENT TRENDS
ANDMODELING OF SAFETY INDICES IN AN INDIAN CONSTRUCTION ORGANIZATION
Sunku Venkata Siva Rajaprasad
KL University, Vaddeswaram & NICMAR, Hyderabad,
India
Email: sunku.vsrp@gmail.com
P. Venkata Chalapathi
Mechanical Engineering Department, KL University,
India
Email: pvrao@kluniversity.in
Submission: 28/10/2015
Accept: 20/02/2016
ABSTRACT
Construction industry has been recognized as a hazardous
industry in many countries due to distinct nature of execution of works.The accident
rate in construction sector is high all over the world due to dynamic nature of
work activities. Occurrence of accidents and its severity in construction
industry is several times higher than the manufacturing industries. The study
was limited to a major construction organization in India to examine the trends
in construction accidents for the period 20082014. In India, safety
performance is gauged basing on safety indices; frequency, severity and
incidence rates. It is not practicable to take decisions or to implement safety
strategies on the basis of indices. The
data used for this study was collected from a leading construction organization
involved in execution of major construction activities all over India and
abroad. The multiple regression method was adopted to model the pattern of
safety indices wise .The pattern showed that significant relationships exist
between the three safety indices and the related independent variables.
Keywords: Frequency rate, severity rate,
incidence rate
1. INTRODUCTION
Due to the high accident rates on
construction sites internationally, strong health and safety legislation has
been devised to minimize accidents and promote construction workers’ safety.
Construction work is one of the most wellknown highrisk occupational areas in
modern society and among the most hazardous, as
measured by workrelated mortality, injury rates, and workers’ compensation
payments (LARSON; POUSETTE;TORNER, 2008).
The
difference in accident rates between developed and developing countries is
remarkable. Proper accident recording and notification systems are nonexistent
in many developing countries (RAJAPRASAD; CHALAPATHI, 2014).
All
stakeholders are responsible with the support of Government to improve accident
rate in Malaysian construction industry and also framed action plan with
recommended actions to minimize accidents. The action plan mainly focused on
safety legislation& enforcement, training, promotional activities and
standards (ABDULLAH; GLORIA, 2010).
Accident
causation theories have enhanced the awareness of causes of accidents and also
emphasized on effect of human mistakes. The theories are failed to offer
extensive strategic guidelines for line managers for reducing risks at
construction sites (HOSSEINIAN; TORGHABEH, 2012). A study conducted in Kuwait construction industry suggested that
accidents are primarily due to management practices rather than human errors,
which is not having large effect (TABTABAI, 2002). Analysis of nature of fall
accidents in construction sites in Indonesia revealed that worker behavior is
contributing to fall injuries(LATIEF, et al., 2011).
Data
pertaining to accident statistics of previous years is necessary to identify
the causes of accidents in the construction sector as the findings would be
more consistent than the results of a questionnaire survey. Efficient safety
practices and mitigation measures may be initiated to eliminate and reduce
reoccurrences in the future with aid of accurate statistical data relating to
accidents. Actual status of construction safety and health issues could be
ascertained by analyzing statistical data of accidents (CHONG; THUAN, 2014).
Lack
of coordination and improper communication between management and employees are
contributing factors of accidents (TEO;LING;CHONG,2005; LAUVER, 2007). Accident
statistics relating to the construction industry manifest that the accident
rate in Malaysian construction industry is high and require a major overhaul
from the current site safety practices (HAMID; MAJID; BACHAN SINGH, 2008).
A study conducted in the USA
indicated that the causes of accidents were due to lack of training and failure
to adopt applicable legislations of safety (Toole, 2002). Accidents in the
construction industry in China is mainly due to insufficient budget for safety;
lack of enforcement of safety regulation and lack of organizational commitment (TAM;
ZENG; DENG, 2004). Safety professionals generally develop safe practices
adopted in similar construction sites and in addition to previous exposure,
data pertaining to accidents is useful for conducting hazard identification and
risk assessment process (GURCANLI; MUNGEN,
2013). A study was conducted in the thermal plant in India basing on the
accident statistics and analyzed safety indices which are useful for continuous
improvement (ABHAYNATH , 2015).
Safety
performance has been monitored by safety metrics and these metrics are useful
to make comparison with industry averages /other organizations. Analyzing
safety metrics over a period of time is vital to identify the trends in
construction industry (HINGE;THURMAN; WEHLE,2013). Regression analysis was
applied to examine the injury rate in Malaysian manufacturing industries and
the results show that there is negative relation between organization size and
injury rate (SAAD; FATIMAH; ZAINIHAN,
2012).
A
comprehensive standard was developed in India during 1983 regarding method for
computation of frequency and severity rates for industrial injuries and
classification of accidents. There is an ambiguity and no single safety indices
will give actual status of safety but still in force. The reason being a
serious accident has a considerable effect on the severity rate but it does not
greatly affect the frequency rate. Many accidents and property damage not
causing man days lost are not properly indicated by safety indices. It is also
not good practice to compare two construction organizations based on their
frequency and severity rates as type of hazards, working conditions, attitude
of employees etc differ from organization. Severity rate does not represent
actual pain and suffering of a worker .Low frequency rate does not mean that
severity rate is also low that is one fatal accident is best example.
Practically
the safety indices are the partial indicators of injuries and no indices is
capable of giving complete overview of safety performance. An attempt has been
made in the present study to draw the trends of safety indices that are
frequency, severity and incidence rates by using multiple regression.
2. SAFETY INDICES
To calculate safety indices it is
required to collect data pertaining to total man hours worked, number of
accidents, man days lost due to an accident and average number persons
employed. In the present study, two more additional variables were also
considered which influence safety performance; number near miss cases and
allocation of safety budget.
2.1.
Man hours
Manhours
worked shall be calculated from the pay roll or time clock recorded including
overtime. When this is not feasible, the same shall be estimated by multiplying
the total mandays worked for the period covered by the number of hours worked
per day. The total number of mandays for a period is the sum of the number of
men at work on each day of the period. If the daily hours vary from department
to department separate estimates shall be made for each department and the
result added together (IS, 1983).
2.2.
Frequency Rate (FR)
Frequency
rate indicate how often do injuries occur. It is calculated both for lost time
injury and reportable lost time injury as follows:
FR = Number of lost time injury x 1 000 000 /
Manhours worked
Loss
time injury is an injury causing disablement extending beyond the day of the
shift on which the accident occurred (IS, 1983).
2.3.
Severity rate (SR)
Severity
rate indicate how serious are the injuries. It is calculated from man days lost
both of lost time injury and reportable lost time injury as follows,
SR = Mandays lost due to lost
time injury x 1 000 000 / Manhours worked
Mandays
lost according to schedule of charges for death and permanent disabilities as
given in Appendix A. In case of multiple injury, the sum of schedule charges
shall not be taken to exceed 6 000 mandays (IS, 1983).
2.4.
Incidence Rate (IR)
Incidence
rate is the ratio of the number of injuries to the number of persons during the
period under review (IS, 1983). It is expressed as the number of injuries per 1
000 persons employed and it is calculated as follows,
IR = Number of losttime
injuries x 1 000 /Average number of persons employed
2.5.
Safety budget
Safety
budget should emphasize the cost of prevention activities against cost of
occurrences of safety related incidents. Managements still of the opinion that
safety budget is cost to the business activity. Allocation of sufficient funds
health and safety is essential to meet requirements of applicable legislations
and to minimize the cost of accidents. In the Chinese construction sector, the
cost of accidents accounts approximately 8.5 % of the total tender price and in
Kuwait it accounts 0.25 2% of project cost (GODWIN, 2011).
2.6.
Near Miss incident
Nearmiss
incident may not injuries and damage to the property. All the near miss cases
are to be reported, investigated and immediately rectified, as the near misses
are indications of accidents in near future. Safety awareness programmes and
trainings are useful tools to educate the employees in identifying work place
hazards and near misses. Each near miss is lessons to safety department as it
clearly expose inherent weaknesses in the system. Researchers have expressed
different opinions whether near miss cases are leading or lagging indicator (TOELLNER,2001;
MANUELE, 2009).
3. METHODOLOGY
The
data used in the present study was collected from a major construction
organization in India for the period 2008 – 2014. The organization under study
involved in execution of major construction activities in both infrastructure
and real estate segments in India and abroad.
The
trends of safety indices that are frequency, severity and incidence rates were
then determined and plotted. The multiple regression method was used to
determine the relationships between frequency rate and number of accidents,
total man hours worked, number of near misses & allocation of safety
budget; severity rate and man days lost, total man hours worked, number of near
misses & allocation of safety budget; incidence rate and average number of
employees employed, total man hours worked, number of near misses &
allocation of safety budget using the statistical package for the social
sciences (SPSS).
3.1.
Modeling construction Accidents
The
multiple regression models were adopted to determine the relationship between
three safety indices (frequency, severity and incidence rates) and relevant
independent variables. The model derived from a study of road accident deaths
in India (Aderamo, 2012).
3.1.1.
Model
for frequency rate
In
present study, the model for frequency rate is shown in equation (1),
FR = MH + NA + NM + BA + e (1)
Where,
FR = Frequency rate, MH = Total man hours, NA = No of loss time accidents, NM =
Number of near miss cases, BA = Budget allocated under safety, e = an error
term
3.1.2.
Model
for severity rate
The
model severity rate is shown in equation (2),
SR = MH + ML + NM + BA + e (2)
Where,
SR = Severity rate, MH = Total man hours, ML = Man days lost, NM = Number of
near miss cases, BA = Budget allocated under safety, e = an error term
3.1.3.
Model
for incidence rate
The
model for incidence rate is shown in equation (3),
IR = NA + PE + NM + BA + e (3)
Where,
IR = Incidence rate, PE = Average number of persons employed, NA = Number of
loss time accidents, NM = Number of near miss cases, BA = Budget allocated
under safety, e = an error term.
The collected data were analyzed with
the Statistical Package for the Social Sciences (SPSS).
4. RESULTS
4.1.
Trend in safety indices
The
data pertaining to safety performance of a construction organization in India
is shown in Table 1 for the period 2008 – 2014. Year wise safety indices are
shown in Table 2.
Table 1: Statistics on accidents
Year 
Man hours (MH) 
No of accidents (NA) 
Man days lost (ML) 
Near misses (NM) 
Budget allocated (BA) 
Average no of persons employed 
2008 
3207879 
21 
124 
54 
1.60 
764 
2009 
4837980 
17 
12200 
12 
0.85 
1152 
2010 
2510798 
10 
6228 
21 
1.75 
598 
2011 
7174088 
20 
218 
44 
2.76 
1708 
2012 
3886768 
27 
12090 
25 
1.58 
925 
2013 
5897771 
38 
18116 
9 
2.44 
1404 
2014 
2633509 
11 
12040 
23 
2.20 
628 
Table 2:
Values of safety indices
Year 
Frequency rate (FR) 
Severity rate (SR) 
Incidence rate (IR) 
2008 
0.153 
0.0260 
27.49 
2009 
0.285 
0.0004 
14.76 
2010 
0.251 
0.0040 
16.72 
2011 
0.358 
0.0330 
11.71 
2012 
0.144 
0.0003 
29.19 
2013 
0.155 
0.0003 
27.07 
2014 
0.239 
0.0002 
17.54 
The
trend in accidents basing frequency rate shows fluctuating with maximum value
during 2011 and the trend relating severity rate reached almost zero for the
period 2012 2014 with maximum severity noticed during 2011. On contrary to the
two indices, the incidence rate is maximum during 2012 and minimum during 2011.
The trends of frequency, severity and incidence rates are shown graphically in
Figure 1, 2 and 3 respectively.
Figure 1:
Trend in frequency rate
Figure 2:Trend in severity rate
Figure 3: Trend in incidence rate
4.2.
Model for frequency rate
The
model for frequency rate is shown in equation (1) and Table 3 shows the
regression summary for frequency rate and the independent variables. The four
independent variables explain 99.82% of the total variation infrequency rate.
The remaining 0.18% is due to the variables which cannot be included in the
model.
Table 3: Regression Summary for frequency rate (*
Significant at 5.0% level)
Dependent Variable 
Independent Variables 
Regression Coefficient 
Standard Error 
tvalues 
Levels of Significance 
Frequency
rate(FR) 
Constant 
0.2244 
0.0119 
18.91^{} 
0.034 
MH 
4.6E08 
2.8E09 
16.00^{*} 
0.039 

NA 
0.0094 
0.0005 
17.35^{*} 
0.037 

NM 
0.0008 
0.0004 
1.757 
0.329 

BA 
0.0087 
0.0068 
1.284 
0.421 
The
regression summary shows that total man hours, number of accidents, number of
near misses and budget allocation has positive association with frequency rate.
Table 4 shows results of analysis of variance and the regression is significant
since the Fstatistic of 136.169 is greater than the critical value of 5.19 at
0.05 level of significance. The tvalues also show that total man hours and number
of loss time accidents are significant at 0.05 level. The coefficient of
determination, R^{2} which is 99.82% shows that the model is a good fit
for the data. The predictive ability of the model is therefore confirmed.
The
regression model obtained is,
FR =
0.2244 +4.6E08MH  0.0094NA  0.0008NM + 0.0087BA (4)
Table 4: Results of analysis of variance –Frequency rate
Source of Variation 
Degrees of freedom 
Sum
of Squares 
Mean
Square 
F
 value 
Regression 
4 
0.0324 
0.0081 
136.169 
Residual 
1 
5.9E05 
5.9E05 

Total 
5 
0.0325 


4.3.
Model for severity rate
The model
for severity rate is shown in equation (2) and Table 5 shows the regression
summary for severity rate and the independent variables. The four independent
variables explain 99.73% of the total variation infrequency rate. The remaining
0.27% is due to variables which cannot be included in the model.
Table 5: Regression Summary for severity rate (*
Significant at 5.0% level)
Dependent Variable 
Independent Variables 
Regression Coefficient 
Standard Error 
tvalues 
Levels of Significance 
Severity rate(FR) 
Constant 
0.0037 
0.0048 
0.764 
0.584 
MH 
3.07E09 
4.1E10 
7.464^{*} 
0.084 

ML 
1.29E06 
2.507 
5.190^{*} 
0.121 

NM 
0.0001 
0.0001 
0.860 
0.547 

BA 
0.0035 
0.0013 
2.588^{*} 
0.234 
The
regression summary shows that total man hours, man days lost number of near
misses and budget allocation has positive association with severity rate. Table
6 shows results of analysis of variance and the regression is significant since
the Fstatistic of 91.765 is greater than the critical value of 5.19 at 0.05
level of significance. The tvalues also show that total man hours man days
lost and budget allocated are significant at 0.05 level. The coefficient of
determination, R2 which is 99.73% shows that the model is a good fit for the
data. The predictive ability of the model is therefore confirmed.
Table 6: Results of analysis of variance – Severity rate
Source of Variation 
Degrees of freedom 
Sum
of Squares 
Mean
Square 
F
 value 
Regression 
4 
0.0009 
0.0002 
91.765 
Residual 
1 
2.3E06 
2.3E06 

Total 
5 
0.0009 


The regression model obtained is,
SR = 0.0037 +
3.07E09 MH  1.29E06ML + 0.0001NM +0.0035 BA
(5)
4.4.
Model for incidence rate
The model
for incidence rate is shown in equation (3) and the Table 7 shows the regression
summary for incidence rate and the independent variables. The four independent
variables explain 99.88% of the total variation in incidence rate. The
remaining 0.12% is due to variables which cannot be included in the model.
Table 7: Regression Summary for incidence rate (*
Significant at 5.0% level)
Dependent Variable 
Independent Variables 
Regression Coefficient 
Standard Error 
tvalues 
Levels of Significance 
Incidence rate(FR) 
Constant 
16.035 
0.833 
19.243 
0.033 
MH 
0.949 
0.038 
25.013^{*} 
0.025 

PE 
0.016 
0.001 
18.808^{*} 
0.033 

NM 
0.204 
0.031 
6.528^{*} 
0.096 

BA 
1.784 
0.475 
3.758^{*} 
0.165 
The
regression summary shows that number of loss time accidents, average number of
persons employed, number of near misses and budget allocation have positive
association with severity rate. Table 8 shows results of analysis of variance
and the regression is significant since the Fstatistic of 209.234 is greater
than the critical value of 5.19 at 0.05 level of significance. The coefficient
of determination, R2 which is 99.88% shows that the model is a good fit for the
data. The predictive ability of the model is therefore confirmed.
Table 8: Results of analysis of variance – Incidence rate
Source
of Variation 
Degrees of freedom 
Sum of Squares 
Mean Square 
F  value 
Regression 
4 
245.629 
61.407 
209.234 
Residual 
1 
0.293 
0.293 

Total 
5 
245.922 


The
regression model obtained is,
SR = 16.035 +
0.949 NA  0.016 PE + 0.204 NM – 1.784 BA (6)
5. CONCLUSION
The
analysis shows the trends in safety indices in an Indian construction
organization. The trends in terms of frequency rate, severity rate and
incidence rate for the period 2008 – 2014 have been examined. The trends of
safety indices are distinct and all the indices shall be given equal importance
while evaluating safety performance.
Implementing
safety systems and strategic decisions relating to safety shall be based on
examining the three safety indices. The reason for including near miss and
safety budget as a variables in regression models is mainly due to their
contribution towards safety performance. Conducting hazard identification and
risk assessment for all construction activities, imparting safety training to
all cadres of employees, developing safety culture as a part of organization
culture and implementing motivational schemes are the drivers to improve safety
indices.
Limitation
of the study being exclusion of cost of accident damages in the regression
models. It is very difficult to generalize cost of an accident, which comprise
two cost components: direct and indirect cost. There is no relationship between
direct and indirect costs but indirect cost is several times more than the
direct cost. The cost of damages is accident specific.
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