Dataset on Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry

Abstract

The data presented in this article is an original data on “Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry”. This article describes the constraints influencing craft gang's productivity and the influence of onsite learning on the blockwork craft gang's productivity. It also presented the method of data collection, using a semi-structured interview and an observation method to collect data from construction organisations. We provided statistics on the top most important constraints affecting the craft gang's productivity using 3-D Bar charts. In addition, we computed the correlation coefficients and the regression model on the influence of onsite learning on craft gang's productivity using the man-hour as the dependent variable. The relationship between blockwork inputs and cycle numbers was determined at 5% significance level. Finally, we presented data information on the application of the learning curve theory using the unit straight-line model equations and computed the learning rate of the observed craft gang's blockwork repetitive work.

Specifications Table

Subject area	Economics, Construction Management, Project management, Management, Quantity surveying and Civil Engineering.
More specific subject area	Construction Project Management
Type of data	Table, Figures.
How data was acquired	Data was acquired by conducting a Semi-structure Interview and observation of the craft gang's in the observed project site.
Data format	Raw, filtered, analyzed.
Experimental factors	We make use of interview and observational data. Our sample was through purposeful.
Experimental features	Data on interview transcript, observed craft gang's man-hour labour productivity.
Data source location	Nigeria.
Data accessibility	The data are available with this article.
Related research article	The data is not related to a companion paper to any research article.

Value of the data

• •The presented data in Fig. 1, Fig. 2, Fig. 3 on the project-specific constraints influencing blockwork craft gang's productivity could inform further research on constraints influencing craft gang's productivity.
• •Craft gangs learning rate productivity determine in Table 3 and Fig. 4 can stimulate further research on craft gang's productivity using U-block, solid walls and curve walls.
• •The data on Fig. 4 and Table 3 are further evidence on the application of the learning curve theory to blockwork craft gang's.
• •The data in this article could be useful to optimise further onsite craft gang's productivity within a project specific environment.

Fig. 1.

Top 5 human/motivation constraints.

Fig. 2.

Top 5 project management constraints.

Fig. 3.

Top environmental, health and safety top constraints.

Table 3.

Learning Rate for Blockwork Craft gangs Productivity.

S/N	LN Man-hours	LN Cycle No	C	D	E	F	G	H	I	J	K	L	M	O	P	Q
	Y	X	XY	X2	n∑XY	∑X∑Y	n∑X2	(∑x)2	E-F	G-H	β=I/J	Ẏ	Ẋ	Βẋ	α=Ẏ-Βẋ	S=2b*100
1	6.1000	–	–	–	9,579.2959	9,591.6441	4,209.9665	3,754.3560	−12.3482	455.6105	−0.0271	6.0208	2.3566	−0.0639	6.0846
2	6.0400	0.6931	4.1863	0.4804
3	6.0300	1.1098	6.6921	1.2317
4	6.0100	1.3862	8.3311	1.9216												98.1389
5	6.0100	1.6094	9.6725	2.5902
6	6.0300	1.7917	10.8040	3.2102
7	6.1200	1.9459	11.9089	3.7865
8	6.0400	2.0794	12.5596	4.3239
9	6.0500	2.1972	13.2931	4.8277
10	6.0200	2.3025	13.8611	5.3015
11	6.0400	2.3978	14.4827	5.7494
12	6.0300	2.4849	14.9839	6.1747
13	6.0400	2.5649	15.4920	6.5787
14	6.0400	2.6390	15.9396	6.9643
15	6.0000	2.7080	16.2480	7.3333
16	6.0100	2.7725	16.6627	7.6868
17	6.0400	2.8332	17.1125	8.0270
18	5.9800	2.8903	17.2840	8.3538
19	6.0100	2.9444	17.6958	8.6695
20	5.9800	2.9957	17.9143	8.9742
21	5.9800	3.0445	18.2061	9.2690
22	5.9900	3.0918	18.5199	9.5592
23	5.9800	3.1355	18.7503	9.8314
24	5.9800	3.1781	19.0050	10.1003
25	6.0100	3.2189	19.3456	10.3613
26	5.9800	3.2581	19.4834	10.6152
∑	156.5400	61.2728	368.4345	161.9218

Fig. 4.

Relationship of blockwork craft gangs productivity and cycle numbers.

1. Data

In this article, first we presented three 3-D bar charts representing the top constraints influencing onsite craft gang's productivity (Fig. 1, Fig. 2, Fig. 3). The correlation coefficient table and the overall regression model between the productive input and its associated cycle number in Table 1, Table 2 was computed using simple linear regression technique. Table 3 present the learning rate of the observed repetitive work activity in the project.

Table 1.

Regression correlation coefficient for blockwork craft gang's.

S/N	LN Man-hours	LN Cycle No	C	D	E	F	G	H	I	J	K	L	M	N	O	P
	Y	X	XY	X2	Y2	n∑XY	∑X∑Y	n∑X2	(∑x)2	n∑Y2	(∑Y)2	H-I	J-K	(L*M)^1/2	F-G	ϒ=O/N
1	6.10	–	0	–	37.2100	9579.295908	9591.644112	4209.966516	3754.35602	24,505.59	24504.7716	455.6104957	0.82	19.28154985	−12.348204	−0.640415532
2	6.04	0.6931	4.18632	0.4804	36.4816
3	6.03	1.1098	6.69209	1.2317	36.3609
4	6.01	1.3862	8.33106	1.9216	36.1201
5	6.01	1.6094	9.67249	2.5902	36.1201
6	6.03	1.7917	10.804	3.2102	36.3609
7	6.12	1.9459	11.9089	3.7865	37.4544
8	6.04	2.0794	12.5596	4.3239	36.4816
9	6.05	2.1972	13.2931	4.8277	36.6025
10	6.02	2.3025	13.8611	5.3015	36.2404
11	6.04	2.3978	14.4827	5.7494	36.4816
12	6.03	2.4849	14.9839	6.1747	36.3609
13	6.04	2.5649	15.492	6.5787	36.4816
14	6.04	2.6390	15.9396	6.9643	36.4816
15	6.00	2.7080	16.248	7.3333	36.0000
16	6.01	2.7725	16.6627	7.6868	36.1201
17	6.04	2.8332	17.1125	8.0270	36.4816
18	5.98	2.8903	17.284	8.3538	35.7604
19	6.01	2.9444	17.6958	8.6695	36.1201
20	5.98	2.9957	17.9143	8.9742	35.7604
21	5.98	3.0445	18.2061	9.2690	35.7604
22	5.99	3.0918	18.5199	9.5592	35.8801
23	5.98	3.1355	18.7503	9.8314	35.7604
24	5.98	3.1781	19.005	10.1003	35.7604
25	6.01	3.2189	19.3456	10.3613	36.1201
26	5.98	3.2581	19.4834	10.6152	35.7604
∑	156.5	61.2728	368.434	161.9218	942.5226

Table 2.

Regression Model for Blockwork Craft gang's.

S/N	LN Man-hours	LN Cycle No	C	D	E	F	G	H	I	J	K	M	N	O
	Y	X	XY	X2	Y2	n∑XY	∑X∑Y	n∑X2	(∑x)2	H-I	F-G	β=K/J	Βẋ	α=Y¯-βẊ
1	6.10	–	0	–	37.2100	9579.295908	9591.644112	4209.966516	3754.35602	455.6104957	−12.348204	−0.0271	−0.063871109	6.08464
2	6.04	0.6931	4.186324	0.4804	36.4816
3	6.03	1.1098	6.692094	1.2317	36.3609
4	6.01	1.3862	8.331062	1.9216	36.1201
5	6.01	1.6094	9.672494	2.5902	36.1201
6	6.03	1.7917	10.80395	3.2102	36.3609
7	6.12	1.9459	11.90891	3.7865	37.4544
8	6.04	2.0794	12.55958	4.3239	36.4816
9	6.05	2.1972	13.29306	4.8277	36.6025
10	6.02	2.3025	13.86105	5.3015	36.2404
11	6.04	2.3978	14.48271	5.7494	36.4816
12	6.03	2.4849	14.98395	6.1747	36.3609
13	6.04	2.5649	15.492	6.5787	36.4816
14	6.04	2.6390	15.93956	6.9643	36.4816
15	6.00	2.7080	16.248	7.3333	36.0000
16	6.01	2.7725	16.66273	7.6868	36.1201
17	6.04	2.8332	17.11253	8.0270	36.4816
18	5.98	2.8903	17.28399	8.3538	35.7604
19	6.01	2.9444	17.69584	8.6695	36.1201
20	5.98	2.9957	17.91429	8.9742	35.7604
21	5.98	3.0445	18.20611	9.2690	35.7604
22	5.99	3.0918	18.51988	9.5592	35.8801
23	5.98	3.1355	18.75029	9.8314	35.7604
24	5.98	3.1781	19.00504	10.1003	35.7604
25	6.01	3.2189	19.34559	10.3613	36.1201
26	5.98	3.2581	19.48344	10.6152	35.7604
∑	156.54	61.2728	368.4345	161.9218	942.5226

Secondly, the data set presented in Table 1, Table 2, Table 3 and Fig. 4 was derived from the observation study. A standard observation sheet and a stopwatch was used in recording the observed time for the craft gang's block laying operation in a working day. The data were collected daily to determine the variation in output for a total number of Twenty-six (26) observations from 7:00 a.m. to 6:00 p.m. daily.

2. Experimental design, materials and methods

The experimental data collection strategies used in this study is standard observation method and semi-structure Interviews.

2.1. Constraints influencing blockwork craft gangs productivity

The data presented in the interview were analysed via content analysis. Computer-assisted content analysis via NVivo 11 pro software was also used to aid the analysis. The participants interviewed were allocated a distinctive set of numbers. The reason for this numbers was for data coding system in order to determine the participant interviewed in the project in the analysis phase of the research. The number begins with the participant given as P, followed by the participant assigned number. For instance, if a participant is allocated with number P01, it means that P is the participant interviewed and was given the number 01.

The experimental design data on the project-specific constraints present the constraint with the highest response and rank. Fig. 1 shows the human/ motivation top project-specific constraints the craft gang's needed to respond to, in order to optimise their productivity.

Fig. 2 shows the top Project management constraints the craft gangs needed to respond to, in order to optimise their productivity.

Fig. 3 shows the top environmental, health and safety constraints the craft gang's needed to respond to, in order to optimise their productivity.

2.2. Influence of onsite learning on blockwork craft gang's productivity

Table 1 shows the data on regression correlation coefficients between blockwork inputs and cycle numbers at 5% significance level. The significance of the correlation coefficients was to determine the relationship between the data and the linear regression model. The coefficients were determined by substituting the linear regression model equation:

$$\mathit{Y}=\alpha +\beta \mathit{X}$$ (1)

The regression equation, α and β indicates the intercept and the slope of the linear regression model. The slope and the intercept are estimated thus;

$$\mathit{\beta }=\left(n\sum \mathit{xy}-\sum \mathit{x}\sum \mathit{y}\right)/\left(\mathit{n}\sum {\mathit{x}}^2-{\left(\sum \mathit{x}\right)}^2\right)$$ (2)

$$\mathit{\alpha }=\stackrel{-}{\mathit{Y}}-\mathit{\beta }\stackrel{.}{\mathit{X}}$$ (3)

where Y, is the man-hours and X, is the Cycle Numbers.

In Table 2, α=6.08, β=−0.03, ϒ=−0.64 as presented in Table 1. Where α is the intercept given by the standard linear equation, β is the slope of the linear curve, ϒ is the correlation coefficient of the observed gangs. Hence, the general regression model for the observed blockwork craft gang's is given below as:

$$\mathit{{\rm Y}}=6.08-0.03\mathit{X}$$

That is, Man hours=6.08–0.03 cycle numbers.

The unit straight-line learning curve model was used to determine the role onsite learning play in the blockwork craft gangs learning productivity. The straight-line unit model is expressed as a power function [1], [2], [3]. The mathematical expressions underlying the logarithmic straight-line learning curve are:

$$\mathit{Y}={\mathit{T}}_{\mathrm{I}}\times {\left(\mathit{x}\right)}^{\mathit{b}}$$ (4)

where Y=cost, man-hours, or time required to perform the repeated unit; T₁=cost, man-hours, or time necessary to perform the first unit; x=cycle number of the unit; and b represents the slope of the logarithmic curve, which is calculated as:

$$b=\frac{\mathrm{InS}}{\ln 2}$$ (5)

where S=learning rate, which is defined as the percentage reduction in the unit input, i.e., cost, man-hours, or time, as a result of doubling the number of units completed. Eq. (5) can be rewritten as:

$$S=(2^b)*100$$ (6)

Fig. 4 and Table 3 presents data on the relationship between craft gang's blockwork and the cycle numbers. We also presented the learning rate (S), expressed as a percentage, this was determined by substituting the slope (b), that is −0.06, into the learning rate equation as follows: S=(2^−0.03)×100.

Transparency document. Supplementary material

Supplementary material

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