Influence of Human Parameters on Labor Productivity in the Construction Industry

Abstract

Objective:

The objective of this article is to investigate the influence of human parameters on qualitative assessment of labor productivity (LP) in the construction industry.

Background:

The theories involving workers have identified various parameters on physical efficiency, such as heart rate, relative heart rate, and calorie count, so as to improve the safety and social conditions of construction labor, thereby increasing LP. However, there is no direct emphasis on assessment of LP using human parameters related to physical strength.

Method:

An exploratory study was conducted on 17 construction workers by observing their task-level LP in real time at a construction site. Human parameters, such as age, body mass index (BMI), handgrip strength, and upper body muscle strength of the laborers, were measured at the construction site. Performance levels of these workers were placed in three categories, and each worker was assigned a typical weightage to each category when correlated to physical strength.

Results:

Labor categories among the human parameters that included middle age, normal-range BMI, and strong muscular strength were shown as having higher LP than others. A quantitative parameter called the Human Parameter Index (HPI) was developed based on the performance categories developed for an individual worker. Human parameters revealed a significant relationship with respect to task-level LP.

Conclusion and Application:

The study determines the influence of human parameters on LP in construction. Introduction of human parameters in the construction industry will help in assessment of LP for various labor-intensive activities.

Keywords

work measurement human performance modeling physiological measurement task analysis work physiology

Introduction

The development of automation and mechanical advancements have been affecting the demand for manual labor in the modern-day construction industry in developing countries, like India. However, manpower utilization in most Indian construction projects is essential to many basic activities, such as brickwork, plastering, and flooring. Construction is a labor-intensive industry in developing countries. Even though there are numerous advancements in the construction industry, various types of construction, such as residential buildings, largely depend on manual labor (Parthasarathy, Murugasan, & Murugesan, 2017). Construction productivity is usually defined as the effective employment of manpower resources (input) in execution services (output) (Thomas & Sudhakumar, 2014). In construction projects, workers perform vigorous activities, such as lifting and carrying construction material; pushing, dragging, and pulling; and loading, all of which involve carrying out difficult work positions and engaging in tiresome activities. Accordingly, workers should be physically strong to withstand these vigorous activities on the construction field.

The construction tasks involve such actions and lead to physical fatigue due to excessive physical strain of workers, which in turn leads to decrease in labor productivity (LP) (Brouha, 1967; Janaro, 1982; National Safety Council, 2000; Raisudeen, Srinivasan, & Venkateskumar, 2014). In similar project locations, LP in construction does not vary (Odesola & Idoro, 2014). Umberto, Giovanni, Susan, and Suzanne (2013) found a relationship between a worker’s physical strain and work productivity using heart rate as a human parameter. Loss of physical strength in humans causes physical strain. Because different people have different physical strength capacities, analyzing human strength–productivity relationships could present a way to estimate LP that can be very helpful in improving the growth of the construction industry.

Physical strength of workers can be measured by human parameters. Physical strain is taken as an indicator to quantify human fatigue and safety, which in turn aids in improving construction LP. Researchers in the past have inferred that the performance of labor is influenced by physical strain or capacity to do work (Abdelhamid & Everett, 1999; Oglesby, Parker, & Howell, 1989). Similarly, past studies were focused on human factors, such as ability to do work, physical fatigue, and physical strain, which depended on human efficiency, that is, physical strength. However, there was no emphasis on assessment of labor performance with respect to physical strength. Therefore, the objective of the present research is to identify, evaluate, and investigate suitable human parameters for performance assessment of labor and correlate it with LP.

Background

Loss of LP in construction on account of workers’ physical strain, fatigue, and ability is widely accepted (Abdelhamid & Everett, 2002; Oglesby et al., 1989; Umberto et al., 2013; Yung, Manji, & Wells, 2017). Numerous approaches and techniques have been developed to measure the physical strength of industrial workers using various human parameters, such as age, body mass index (BMI), heart rate, relative heart rate, breath rate, and handgrip strength (HGS) (Koley, Kaur, & Sandhu, 2009; Umberto et al., 2013; Yuan, Buchholz, Punnett, & Kriebel, 2007). Low physical strength in humans is one of the factors that affect work performance (Astrand, Rodahl, Dahl, & Stromme, 2003). A study on HGS of female construction workers was conducted in India to find the required physical strength to perform various activities, and productivity of these female workers was evaluated by HGS (Koley et al., 2009). Chilima and Ismail (2001) reported that people with unusual BMI had lower HGS. Yuan et al. (2007) described an integrated approach to ergonomic interventions for construction workers that involves upper-body muscles, such as low back and shoulder, during wall installation. Therefore, physical demands depend mainly on human physical strength.

In general, physical efficiency of humans is characterized by age and BMI. The process of aging in humans leads to significant changes in body muscle mass, and therefore loss of physical strength occurs (Keller & Engelhardt, 2013). Physical fitness of the human body in older people is reduced, causing difficulties in performing tasks (Donat, Ozcan, Malkoc, & Aksakoglu, 2009). The physical fitness of the human body can apparently be assessed using isometric strength tests. These tests involve a maximum controlled contraction performed at a specified body joint angle of humans in a stationary position. An HGS test is an isometric strength test carried out by handgrip, involving hand and forearm muscles (Koley et al., 2009). The frequency of upper-extremity muscle activity is high in humans while they perform continuous tasks (Gruevski, Hooder, & Keir, 2017). Therefore, a new parameter is introduced in the present study, that is, upper-body muscle strength (UBMS). This measurement is similar to HGS. In UBMS, strength of the various upper-body muscles, such as shoulder, back, and chest, is involved. Physical strength changes with change in body mass, which is represented by BMI. The index of an individual’s body fat is represented by BMI (Nuttall, 2015). Based on anthropometric height and weight characteristics, BMI is used to categorize humans. BMI is calculated by dividing the body mass by the square of the height of a person and is usually represented in kg/m².

Method

Strength Tests

Strength tests such as HGS and UBMS are related to human postures. The measured values from these tests are the human parameters related to physical strength. Strength in one working posture does not accurately predict strength in another posture. The force applied by hand to pull objects is defined as grip strength. The forceful bending and tightening of all finger joints with a great force applied under normal biokinetic conditions is known as the power of the grip (Bohannon, 1997; Richards, Olson, & Palmiter, 1996). Construction workers require bending and twisting of various upper-extremity body parts, such as back, neck, and shoulder, and therefore UBMS is used in assessment of physical strength.

The process of conducting the strength tests in the present investigation was designed carefully to simulate determined construction tasks. These tests were performed with a strain gauge, cable tensiometer, hand dynamometer, or similar device that records applied static force. Maximum static force applied and the average rate of force development were displayed and recorded (Kroemer, 1970). These tests show high consistency in both single- and multijoint body parts in humans. The maximum static force applied by humans was more acceptable than the average rate of force development (Wilson & Murphy, 1996).

In every project, sufficient workload was planned and assigned to the construction laborers on-site. Assessing workload capacity at the construction task level provides a sensitive measure of human performance (Yamani, 2016). As the laborers engaged in construction activity undergo most hand and upper-body movements at task level, HGS and UBMS tests were chosen for the present study. These parameters were in turn influenced by general factors, such as age and BMI. Both the right-hand and left-hand grip strength is positively interrelated to BMI (Chatterjee & Chowdhuri, 1991). HGS is found to have a positive correlation with body size and physical task, which determines the human physical strength. These tests have been used to evaluate the physical strength of the worker. In the case of masonry work, any specific construction task involves upper-body movements. Therefore, the UBMS test is considered in various specified postures that can determine the physical strength of the worker. These postures require investigation by observing the most-repeated task motions in the construction field.

Human Parameters

Age

Influence of age on performance of workers is generally greater in older people. Among individual workers of different ages, productivity increases until the age of 35 and slightly declines thereafter (Skirbekk, 2008). The quality or speed of the work does not necessarily reflect the productivity of workers individually. Skirbekk (2008) determined age variation in LP based on various physical levels of strength of workers. Measuring productivity based on age is determined by the physical ability to perform the task. In sampling, workers are selected in terms of their age categories (Rubin & Perloff, 1993). The productivity of workers with respect to specific age category is given as

{\hat{p}}_{a, c} = \frac{Σ p_{a, c}}{n} .

(1)

In Equation (1), ${\hat{p}}_{a, c}$ represents average productivity of workers in which a stands for age and c stands for category; p_a,_c represents the individual productivity of a worker, and n is the number of workers in the respective age category. A typical trend of LP with respect to increase in age is shown in Figure 1.

Figure 1 shows the influence of age of the subjects (workers) starting from younger age to older age on their LP. Based on their age, subjects are categorized as middle age (a,c₁), younger age (a,c₂), and older age (a,c₃). Maximum-level physical ability based on human strength is generally observed in the younger and middle age categories. Noticeable changes in the process of human aging will be observed in the older age category. Middle-aged people have relatively smaller changes in body mass and physical strength, and therefore productivity is high.

BMI

The influence of body mass and size on physical tasks in general is an established factor (Astrand, Rodahl, Dahl, & Stromme, 2003; Keating & Matyas, 1996; McMahon, 1984; Nevill, Ramsbottom, & Williams, 1992; Vanderburgh, Mahar, & Chou, 1995; Wilson & Murphy, 1996). However, BMI depends only on change in weight of an adult person. The most commonly used categorizations of BMI as per the World Health Organization (WHO) are shown below in Table 1.

Table 1:

Body Mass Index (BMI) Categorization as per World Health Organization

BMI (kg/m²)	Categorization
<18.5	Underweight
18.5–24.9	Normal weight
25–29.9	Overweight
30–34.9	Class I obesity
35–39.9	Class II obesity
≥40	Class III obesity

Source. Retrieved from http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi. Copyright World Health Organization, United Nations (2016).

The productivity of workers with respect to BMI category is given as

{\hat{p}}_{b, c} = \frac{Σ p_{b, c}}{n} .

(2)

In Equation (2), ${\hat{p}}_{b, c}$ represents average productivity of workers in which b stands for BMI, and c stands for category; p_b,_c represents the individual productivity of a worker, and n is the number of workers in the respective BMI category. A typical trend of LP with respect to BMI is shown in Figure 2.

Figure 2 shows the influence of BMI of the subjects on their LP. Based on their BMI, subjects are classified as normal-weight BMI (b,c₁), lower-weight BMI (b,c₂), and BMI with higher weight (b,c₃). Maximum-level physical ability is generally observed among workers in the normal-weight BMI category. Performance level of workers gradually dips in people who are overweight or underweight in terms of BMI.

HGS

This measure is an isometric strength test that is conducted to find the efficiency of a grip involving hand and forearm muscles of a human. The productivity of workers with respect to the specific HGS category is given as

{\hat{p}}_{h, c} = \frac{Σ p_{h, c}}{n} .

(3)

In Equation (3), ${\hat{p}}_{h, c}$ represents average productivity of workers in which h stands for HGS and c stands for category; p_h,_c represents the individual productivity of a worker, and n is the number of workers in the respective HGS category.

Figure 3 shows the influence of HGS of the subjects on their LP. Based on their HGS, subjects (workers) are classified as higher strength (h,c₁), medium strength (h,c₂), and lower strength (h,c₃). Performance of workers linearly increases with increase in HGS (Koley et al., 2007).

UBMS

This measure is another isometric strength test that is conducted to find the efficiency of UBMS involving shoulder and low-back muscles of a person. The productivity of workers with respect to the specific UBMS category is given as

{\hat{p}}_{u, c} = \frac{Σ p_{u, c}}{n} .

(4)

In Equation (4), ${\hat{p}}_{u, c}$ represents average productivity of workers in which u stands for UBMS and c stands for category; p_u,_c represents the individual productivity of a worker, and n is the number of workers in the respective UBMS category. A typical trend of LP with respect to UBMS is shown in Figure 4.

Figure 4 shows the influence of UBMS of the subjects on their LP. Based on UBMS, subjects (workers) are classified as higher strength (u,c₁), medium strength (u,c₂), and lower strength (u,c₃). Performance of workers linearly increases with increase in UBMS.

Human Parameter Index (HPI)

A major shortfall in the research so far is that it has not been able to relate human strength to the ability to quantify the amount of physical task performed by the worker. Measurement of work in relation to human parameters can be utilized in assessing performance of construction workers. General parameters, such as age and BMI, alone cannot be taken to represent human performance; therefore, earlier researchers have considered HGS as a human parameter to represent human performance (Bohannon, 1997; Koley et al., 2007; Richards et al., 1996). In the present research, a new parameter called UBMS is added to assess human performance. Most of the researchers have considered various human parameters, such as age, BMI, or heart rate, individually to assess human strength and relate it to LP. A single ability or a single parameter can never absolutely be taken as a yardstick to assess LP. Hence, multiple human parameters can collectively represent human strength in assessing LP, and this is characterized as the HPI. The sum of the weightages of human parameters of an individual worker corresponding to performance category is given as

\begin{array}{l} H P I = Σ_{i = 1}^{k} {W^{i}}_{c_{n}} \\ = {W^{1}}_{c_{n}} + {W^{2}}_{c_{n}} + {W^{3}}_{c_{n}} \dots \dots \dots \dots + {W^{k}}_{c_{n}}, \end{array}

(5)

where ${W^{i}}_{c_{n}}$ represents the performance weightage of an individual laborer with respect to the human parameter; i =1, 2, 3 . . . k represents various human parameters; and c_n = c₁, c₂, . . . c_n represents different categories in the respective human parameter.

HPI is determined based on the sampling of group data, such that the physical strength of an individual worker calculated by this index method represents the overall sample of data. Human parameter data of construction labor are collected from field observation, and individual parameter weightages are given depending on their respective category. The sum of the weightages of human parameters of each worker gives the HPI for the respective worker. Because the human parameters illuminate the influence of physical strength on productivity of workers, it is possible to express the relationship between human strength (i.e., HPI) and LP (Umberto et al., 2013). Devices for measuring the human parameters in the present study are shown in Figure 5.

Field Investigation

An exploratory field study was conducted at a residential building construction site in Warangal, India. Site supervisors assisted in selecting suitable labor crews for carrying out the study. The workers were working at the site over a period that was sufficient to help the investigators to carry out trials before starting the actual field experiment. Seventeen workers among three crews were involved in the field experiment, including three female workers. The age group of workers varied between 18 and 52 years. Experimental trials were conducted over a period of 3 to 4 months. Trials were conducted on specific days without any severe climatic disturbances. Care was taken to ensure that all the workers were in good health during the time of field trials.

A brick wall construction activity using autoclaved aerated concrete (AAC) blocks was chosen for the study. Video studies were used to record the performance of the workers. A total of 31 observations of around 2,000 min were recorded successfully on-site during the days when the activities were in full progress. There were no disturbances, such as bad climatic conditions, nonavailability of resources, and so on. Only productive work of each worker was considered in the present study. LP is measured in square feet built per 30 min.

Results

Collection of Human Parameters Data

Data regarding productivity for an individual worker were calculated from the video observations recorded at the construction site. Each video observation contains a group of workers performing the given tasks. Human parameters data were also collected at the site itself. The personal details of workers, such as name, age, and so on, were collected through an on-site interview. Weight and height of the workers were measured using an auto-calibrated electronic weighing machine (5–180 kg range) and stature meter (2 m length), from which BMI was calculated. The categories of the collected data are calculated as

\begin{matrix} \bar{x} \pm 0.5 σ \\ where \bar{x} - 0.5 σ = Lower Category (LC) \\ \bar{x} + 0.5 σ = Upper Category (UC) \\ \begin{array}{l} between \\ ″ \bar{x} - 0.5 σ ″ & ″ \bar{x} + 0.5 σ ″ = Mid Category (MC) \end{array} \end{matrix}]

(6)

From Equation (6), $\bar{x}$ is the average and σ is the standard deviation of the collected data. The average age parameter for all workers was calculated from the collected data through personal interview. Height and weight of the workers were measured by stature meter and electronic weighing machine, respectively, and BMI was calculated as weight divided by the square of the height. It was observed that the construction tasks on-site involved mostly hand movements and upper-body movements. Therefore, isometric strength tests, such as HGS and UBMS, were chosen for the present study. A digital hand dynamometer was used to collect HGS, and UBMS was measured using a handgrip digital isometric trainer. From these tests, the worker’s muscle strength was recorded at three time points during the entire work process, that is, before starting work, during work, and after the completion of work.

The HGS test was performed while the worker was in standing position with shoulder adducted and neutrally rotated with elbow extended. Workers were asked to put their maximum force to press the hand dynamometer during the trials. The average of three trials conducted with both left hand and right hand was recorded in pounds. The average of 18 values (2 hands × 3 trials × 3 intervals) of grip strength collected from workers is considered as HGS for the respective worker, shown in Table 2.

Table 2:

Human Parameters and Labor Productivity of Workers From Construction Site

Worker Number and Gender	Height(m)	Weight(kg)	BMI(kg/m²)	Age(years)	HGS(lbs)	UBMS(lbs)	Productivity(ft²/30 min)
1. M	1.60	70.3	27.5	52	74.1	16.4	11.9
2. M	1.75	77.0	25.1	37	65.3	18.0	11.8
3. M	1.65	64.0	23.5	26	84.6	23.2	13.5
4. M	1.62	60.4	23	32	79.5	26.7	13.0
5. M	1.63	51.6	19.4	31	90.8	23.4	15.0
6. M	1.78	59.0	18.6	35	87.3	25.2	13.9
7. M	1.65	65.5	24.1	35	95.9	22.6	12.1
8. M	1.64	68.5	25.5	25	79.5	20.2	11.9
9. F	1.54	60.6	25.6	30	55.8	10.7	6.1
10. F	1.56	61.5	25.3	45	65.3	16.1	9.4
11. M	1.64	49.6	18.4	23	78.6	21.9	6.6
12. M	1.59	44.5	17.6	24	80.7	25.9	7.5
13. M	1.75	55.0	18	25	88.3	17.7	11.1
14. M	1.72	75.0	25.4	37	87.2	22.9	7.0
15. M	1.71	61.0	20.9	23	87.2	28.2	13.3
16. F	1.49	39.0	17.6	45	48.1	12.3	5.4
17. M	1.69	47.2	16.5	18	70.8	19.1	11.9
Average ( $\bar{x}$ )				31.9	77.6	20.6	10.7
SD (σ)				9.2	13.0	5.0	3.0
Lower category				18–26	48.1–71.1	10.7–18.1	5.4–9.2
Middle category				26–36	71.1–84.1	18.1–23.1	9.2–12.2
Upper category				36–52	84.1–95.9	23.1–28.2	12.2–15.0

Note. BMI = body mass index; HGS = handgrip strength; UBMS = upper-body muscle strength; M = male; F = female.

Similar to the real-time posture analysis done by Ray and Teizer (2012) on construction workers, the UBMS test in the present study was conducted by taking three different postures, that is, chest pose, wall climb pose, and head pose, while in standing position. Workers were asked to exert maximum force to pull and push by holding the handgrip isometric trainer for 6 s (the device contains alarm settings for 6- to 20-s hold). Average peak force is achieved with the device by holding it for 6 s, and so repeated trials are avoided. The average of 18 values (3 poses × 2 forces × 3 intervals) was recorded as UMBS for the respective worker in pounds and is shown in Table 2.

Mean and standard deviation of each parameter was calculated, as shown in Table 2. Workers were categorized using Equation (6). However, in the case of BMI, the categories given by WHO were adopted. The human parameters and respective LP of construction workers measured from the site are shown in Table 2.

Categorization of Labor

The LP and respective human parameters were observed for 17 workers at a construction site while carrying out wall construction activity using AAC blocks; these data are summarized in Table 2. Based on Equation (6), categories are shown for the human parameters and LP of workers in Table 2. Performance weightages are calculated based on observed LP data collected from the field. Minimum and maximum LP of the collected workers’ data are 5.4 and 15.0 square feet per 30 min, respectively. Performance weightages based on the LP were calculated and are shown in Table 3. From Table 3 column C, the lower and upper limits of the productivity categories are taken as shown in Table 2, and these limits are normalized from the maximum value of LP as shown in column D of Table 3. Average values of normalized lower limit and upper limit are considered as performance weightage (c_n) of labor in their respective category. Considering 100% for best performance, average and low working performances were marked as 75% and 50%, respectively. Therefore, typical performance weightages, such as c₁, c₂, and c₃, are proposed as 1.00, 0.75, and 0.50, respectively. Categorization of labor based on LP is shown in Table 4.

Table 3:

Calculation of Performance Weightages With Respect to Labor Productivity

A	B		C		D
Category	Productivity (Equation [6])		Normalization (b/15.0)		Performance Weightage (c_n) = Average (Lower Limit, Upper Limit) From Column C
	Lower Limit	Upper Limit	Lower Limit	Upper Limit	Obtained	Proposed
c ₁	5.4	9.2	0.359	0.612	0.49	0.50
c ₂	9.2	12.2	0.612	0.811	0.71	0.75
c ₃	12.2	15.0	0.811	1.000	0.91	1.00

Table 4:

Classification of Human Parameters With Respect to Productivity

Human Parameter(i)	Performance Category(Equation [6])	Performance Category(c)	Average Productivity	Category (c_n)
Age (years)(1)	LC	${\hat{p}}_{A, (< 26)}$	10.4	c ₂
	MC	${\hat{p}}_{A, (26 - 36)}$	12.3	c ₁
	UC	${\hat{p}}_{A, (> 36)}$	9.1	c ₃
BMI (kg/m²)(2)	LC	${\hat{p}}_{B, (< 18.5)}$	8.5	c ₃
	MC	${\hat{p}}_{B, (18.5 - 24.9)}$	13.5	c ₁
	UC	${\hat{p}}_{B, (> 24.9)}$	9.7	c ₂
HGS (lbs)(3)	LC	${\hat{p}}_{H, (< 71)}$	8.9	c ₃
	MC	${\hat{p}}_{H, (71 - 84)}$	10.2	c ₂
	UC	${\hat{p}}_{H, (> 84)}$	12.3	c ₁
UBMS (lbs)(4)	LC	${\hat{p}}_{U, (< 18.2)}$	9.3	c ₃
	MC	${\hat{p}}_{U, (18.2 - 23.2)}$	10.5	c ₂
	UC	${\hat{p}}_{U, (> 23.2)}$	12.5	c ₁

Note. BMI = body mass index; HGS = handgrip strength; UBMS = upper-body muscle strength; LC = lower category; MC = middle category; UC = upper category.

Influence of Human Parameters

In order to represent the data, plotting was done according to the nature of the data collected and analyzed. However, the focus of the present work is to study the nature of the parameters within the categories of LP. Therefore, categorical data were plotted to understand the influence of various human parameters of workers on their productivity, as shown in Figure 6.

Age had a reasonable effect on productivity. From Figure 6a, it is observed that middle-age workers showed higher productivity, whereas older workers showed low productivity. Also, younger workers showed considerably high productivity compared with older workers. Therefore, middle-age workers can be taken as highly productive on the construction field and are given as category c₁; subsequent categories are given as category c₂ and category c₃, as shown in Table 3.

Productivity of workers against BMI categories as per WHO was plotted as shown in Figure 6b. Maximum productivity showed in the normal-weight category. Both overweight and lesser-weight categories showed low productivity; the lesser-weight category is showing the least productivity. Normal-weight category workers are given as category c₁, whereas overweight and underweight workers are given as category c₂ and category c₃, respectively.

The measurement of HGS and UBMS has a good prognostic value. Productivity variation is shown in Figures 6c and 6d. It is observed that productivity of workers linearly increases with increase in muscle strength. Based on the average productivity of workers in the particular categories of both isometric strength tests, the workers in the upper category are treated as the best performers and hence are given as category c₁ workers, and subsequent categories are given as category c₂ and category c₃.

The typical performance weightages for workers were assigned to the human parameters based on their respective categorization, shown in the Table 5. The sum of these weightages of a respective worker constitutes HPI. The maximum and minimum values of HPI that a worker can have is 4 and 2, respectively, because the present study involves only four human parameters. The correlation between LP and HPI was examined. Then LP was found well correlated in terms of Performance Index (PI) of a worker and was obtained by multiplying normalized LP of a worker with performance weightage. PI of a worker is defined as

\begin{array}{l} PI = Norm (LP) \times {W^{i}}_{c_{n}} \\ PI = \frac{LP}{LP \max} \times {W^{i}}_{c_{n}} \end{array}

(7)

Table 5:

Calculation of Performance Weightages, HPI, and PI of Workers

Worker Number	Performance Weightages as per Category $({W^{i}}_{c_{n}})$								HPI $(Σ_{i = 1}^{4} {W^{i}}_{c_{n}})$	PI $(\frac{LP}{LP \max} {W^{i}}_{c_{n}})$
Worker Number	BMI		Age		HGS		UBMS		HPI $(Σ_{i = 1}^{4} {W^{i}}_{c_{n}})$	PI $(\frac{LP}{LP \max} {W^{i}}_{c_{n}})$
1	c ₂	0.75	c ₃	0.50	c ₂	0.75	c ₃	0.50	2.50	1.98
2	c ₂	0.75	c ₃	0.50	c ₃	0.50	c ₃	0.50	2.25	1.77
3	c ₁	1.00	c ₂	0.75	c ₁	1.00	c ₁	1.00	3.75	3.38
4	c ₁	1.00	c ₁	1.00	c ₂	0.75	c ₁	1.00	3.75	3.25
5	c ₁	1.00	c ₁	1.00	c ₁	1.00	c ₁	1.00	4.00	4.00
6	c ₁	1.00	c ₁	1.00	c ₁	1.00	c ₁	1.00	4.00	3.71
7	c ₁	1.00	c ₁	1.00	c ₁	1.00	c ₂	0.75	3.75	3.03
8	c ₂	0.75	c ₂	0.75	c ₂	0.75	c ₂	0.75	3.00	2.38
9	c ₂	0.75	c ₁	1.00	c ₃	0.50	c ₃	0.50	2.75	1.12
10	c ₂	0.75	c ₃	0.50	c ₃	0.50	c ₃	0.50	2.25	1.41
11	c ₃	0.50	c ₂	0.75	c ₂	0.75	c ₂	0.75	2.75	1.21
12	c ₃	0.50	c ₂	0.75	c ₂	0.75	c ₁	1.00	3.00	1.50
13	c ₃	0.50	c ₂	0.75	c ₁	1.00	c ₃	0.50	2.75	2.04
14	c ₂	0.75	c ₃	0.50	c ₁	1.00	c ₂	0.75	3.00	1.40
15	c ₁	1.00	c ₂	0.75	c ₁	1.00	c ₁	1.00	3.75	3.33
16	c ₃	0.50	c ₃	0.50	c ₃	0.50	c ₃	0.50	2.00	0.72
17	c ₃	0.50	c ₂	0.75	c ₃	0.50	c ₂	0.75	2.50	1.98

Note. BMI = body mass index; HGS = handgrip strength; UBMS = upper-body muscle strength; HPI = Human Parameter Index; PI = Performance Index.

The PI of workers is calculated from Equation (7) and is shown in Table 5. From Figure 6, the present study clearly shows that LP is influenced by physical strength. It should come as no surprise that workers having lower physical strength will show low LP and may not equally perform compared with workers having higher physical strength. However, a few workers may perform well with lower physical strength due to their better motivation toward work as an exception. Therefore, the influence of human parameters on LP will have a significant relationship. This relationship is explained with regression analysis between HPI and PI of workers, as shown in Figure 7.

The PI and HPI of 17 workers are plotted in Figure 7. With R² value (0.71), the plot shown in Figure 7 clearly indicates that PI is significantly influenced by HPI. Therefore, PI increases with increase in HPI of workers. The regression curve is fitted and the equation is

y = 0.2659 x^{1.8612},

where y is the PI of an individual worker and x is the HPI.

PI = 0.2659 {(HPI)}^{1.8612} .

(8)

Because PI is the normalized LP multiplied by HPI, LP of a worker is calculated by dividing HPI on both sides for Equation (8). LP of a worker is given as

Norm(LP) × HPI/HPI = 0.2659(HPI)^1.8612/HPI

LP/LPmax = 0.2659(HPI)^1.8612–1

LP = 0.2659(HPI)^0.8612 × LPmax

The above equation is simplified and given as

LP = 0.3 (LPmax) {(HPI)}^{0.9}

(9)

Because the present work involved an exploratory case study, the above model cannot be generalized. But the outcome clearly implies that the standardized model can be achieved with further studies on similar types of various construction activities and with a greater number of subjects. Changes in construction workforce on-site may include higher percentages of women and older workers, for whom excessive physical demand will play a major role in achieving higher LP. In addition, compared with male workers, female workers have lower physical strength, forcing them to work at higher rates, which makes them more vulnerable to fatigue.

Conclusion

The present study contributes to knowledge about the utilization of human parameters related to physical strength in qualitative assessment of LP in the construction industry. The influence of human parameters on LP was examined in carrying out wall construction activity. Human parameters, namely, age, BMI, HGS, and UBMS parameters, together were found to be good indicators in arriving at LP. The findings revealed that the subjects (workers) are categorized with respect to human parameters based on their level of performance, such as lower (c₃), middle (c₂), and upper (c₁) categories. Human parameters within these three categories showed promising trends on LP. Therefore, the performance of labor at a construction site can be assessed from these three categories. For this purpose, a quantitative parameter called HPI was developed to identify the individual performance level of a worker. Statistical analysis revealed a significant relationship between physical strength and productivity of construction labor.

Nevertheless, the research study may be affected by several limitations within the construction projects, such as limited subjects in the present study and performance of a single activity, that is, brick wall construction. It is worth mentioning that the study carried out has limited applicability and may not be generalized. However, qualitative conclusions were derived as the subjects were skilled and were observed in a real-time construction process.

Even though the results of the study are not conclusive in determining the effect of physical strength on task-level productivity and also not sufficient to develop a management tool to assess task-level productivity, this approach offers a new method of utilizing human physical strength parameters for task-level productivity assessments in construction projects. Therefore, the outcome of the present study will support the advancement of estimations on task-level labor performance. The existing research on/knowledge of human parameters contributes to workers’ well-being and safety so that better productivity is achieved. However, there is no research that has been able to quantify worker performance with human parameters. Therefore, the present study opens the arena for establishing a relationship between human physical parameters and productivity of construction workers. Furthermore, the methods, outcomes, and suggestions for further research in the direction of the present study will not only encourage advances in LP assessment but also enhance developments in LP and lead to better-quality work performance.

The applications in the future would be toward optimization of work schedules based on worker performance, optimization of suitable workforce selection for labor-intensive activities, and development of effective LP assessments. This research accounted for human parameters that would assist in assessing LP and furnish a new method that serves construction firms to estimate LP and manage the required workforce capabilities.

Key Points

Four human parameters—age, body mass index, handgrip strength, and upper-body muscle strength (UBMS)—related to human physical strength were together used to assess the physical strength of a construction worker.

UBMS is a new isometric strength parameter introduced in the present research and was found suitable as a parameter for assessment of human physical strength.

Typical weightages were established from the performance categories of labor to quantify each human parameter in such a way as to correlate it with labor productivity (LP).

A quantitative index parameter called the Human Parameter Index was developed to qualitatively assess LP in construction.

The present study opens the arena for bringing out a relationship between human physical parameters and productivity of construction workers.

Footnotes

Acknowledgements

The authors would like to thank the Department of Civil Engineering, National Institute of Technology, Warangal, for providing research facilities to carry out this research work.

Dasari Karthik is a PhD scholar in the Department of Civil Engineering, National Institute of Technology (NIT), Warangal, India. He earned his master of technology in construction technology and management from NIT in 2013 and has practical exposure because of his professional job roles and responsibilities, such as planning engineer, project engineer, and project manager in various construction companies in India.

C. B. K. Rao is a professor in the Department of Civil Engineering, National Institute of Technology, Warangal, India. He is specialized in engineering structures, ferrocement, fiber-reinforced concrete, sustainable concrete, torsion of reinforced concrete members, rehabilitation and retrofitting of structures, remedial engineering, ductility of high-strength concrete, and progressive collapse.

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