Influence of Labour Experience in the Generation of Construction Material Waste in the Sri Lankan Construction Industry

Abstract

The construction industry consumes a huge quantity of raw materials, some of which ends up as waste in the construction process. Among many factors, studies suggest that the inexperience of labour is one factor that generates construction material waste. However, an in-depth analysis of construction material waste generation concerning the different levels of labour experience has not been undertaken. Thus, this study investigates the influence of labour experience in the generation of brick and tile material waste in the Sri Lankan construction industry and, thereby, develops a model to predict average waste generation with respect to labour experience. Bricks and tiles were considered since they are identified as the materials most wasted in the Sri Lankan construction industry. To carry out this research, nine similar commercial projects under construction using bricks and tiles were selected from three large building construction organizations in Sri Lanka. Non-participant direct observations and unstructured interviews were adopted as data collection techniques. A simple arithmetical mean method was adopted to analyse material wastage and a scatter diagram was used to identify the correlation and regression to develop a prediction model. The findings revealed that, when labour experience increased, brick and tile wastage generation decreased, although there were slight fluctuations.

1. Introduction

Growth of the population, increasing urbanization, and rising standards of living due to technological innovations have contributed to increasing the magnitude of solid waste generation by construction, mining, and domestic and agricultural activities [1]. The construction industry generates a vast quantity of waste which is environmentally unfriendly and costly to project budgets [2]. Elshaboury and Marzouk [3] stressed that construction waste is approximately 35% to 50% of all global waste. Yuan and Shen [4] warned that world cities are generating about 1.3 billion tonnes of solid waste per year and that this volume is expected to rise to 2.2 billion tonnes by 2025. Therefore, it is envisaged that construction waste will be a forthcoming burden to all countries in the world.

A study by [5] classified construction waste into three categories: material, labour, and mechanical waste. Jain [6] (p. 57) emphasized, “Building materials waste is difficult to recycle due to high levels of contamination and a large degree of heterogeneity”. Studies have shown that the construction industry produces a large amount of waste and between 13% and 60% of waste material is deposited in landfill in each country [7]. A study by [8] stressed that material waste has a significant impact on the cost of a project as well as an adverse impact on the environment, such as through creation of health hazard problems, soil and water contamination, and deterioration of the landscape by uncontrolled landfills. It has become a burden to clients, as they have to bear the costs of waste, as well as to contractors, which can eventually lead to loss of profits and to bankruptcy [9]. Construction material waste (CMW) is contributing to the speedy reduction and ineffective use of natural resources and energy and, consequently, affecting the availability of landfill [10]. The significant amount of construction material waste generated by the construction industry is costly and environmentally unfriendly [11]. Therefore, proper management of construction material waste is vital through the enactment and implementation of policies to ensure the conservation of natural resources, decreased costs and minimisation of the impacts of waste disposal. In this context, the identification of sources of waste generation, as well as the factors influencing material waste, is vital.

According to [12], CMW source evaluation classifies waste sources in four categories relating to design, construction, material handling, and procurement. Further, the report suggests that, during the operational stage, construction wastage occurs due to poor workmanship and inexperienced labour, inadequate tools, and equipment, poor working conditions, and inadequate supervision. Specifically, References [5,13] highlighted labour experience as a cause of CMW in their studies. Thus, it was considered that labour experience (LE) is a crucial factor affecting CMW generation. The need for a deeper and broader understanding of this was the point of departure for this research. Hence, this study aims to investigate the influence of labour experience on the generation of material waste in the Sri Lankan construction industry and to develop a model to predict average waste generation in relation to labor experience. The scope of the study is limited to bricks and tiles as they have been identified as the materials most wasted in the Sri Lankan construction industry.

Thus, as an initial step, this research set out to explore the influence of labour experience in CMW, focusing only on brick and tile material waste. The following key research questions were addressed:

RQ1. How much brick and tile CMW is generated during the construction stage?

RQ2. What is the correlation between brick and tile CMW and LE?

First, the key findings of a literature review are discussed. Thereafter, the paper presents the methodology of the study followed by the research findings. The paper concludes with answers to the research questions along with recommendations for practitioners.

2. Literature Review

2.1. Construction Material Waste

Viana, Formoso, and Kalsaas [14] divided construction wastage into two categories: direct waste, which is the loss of materials, damaged and subsequently used or lost during the building process, and indirect waste, distinguished from direct waste because it is a monetary loss and materials were not lost physically. According to [1,15], construction waste can be divided into three principal categories: material, labour, and machinery waste.

Building material wastage is defined as the difference between the value of materials delivered on-site and those properly used in accurately measured work [16]. Material wastage is of more concern since most raw materials as construction inputs come from non-renewable resources [1,14]. A study by [17] highlighted figures of CMW in Sri Lanka, for example, for sand 25 %, lime 20%, cement 14%, bricks 14%, and ceramic tiles 10%. According to the authors, the high percentages reported were mainly due to cutting waste resulting in different sizes and uneconomical shapes, and the management of waste associated with incorrect decision-making and lack of supervision. Shahid, Thaheem, and Arshad [18] found that cement (33%), timber (32%), sand (28%), brick (12%), and ceramic tiles (11%) had high percentages of material wastage in Pakistan. In Hong Kong, Reference [19] revealed that material wastage of bricks, tiles, cement, sand, and aggregates came in the top five in terms of cost. Hence, it is apparent that, when considering material cost and weight, bricks and tiles are two of the most wasted materials in the construction industry.

Waste represents the losses caused by activities that generate direct or indirect costs, but which do not add any value to the product from the point of view of the client [20,21,22]. Almusawi, Karim, and Ethaib [23] pointed out that the large volumes of construction waste strain landfill capacities and lead to environmental concerns in many countries. Construction waste is undesirable and impractical economically, socially, and environmentally.

Construction waste management is an essential component of sustainable construction [5,24]. Further, Reference [5] stressed that waste management concerns avoiding waste where possible, decreasing waste where feasible, and reusing resources that would otherwise be wasted. With careful identification of construction waste sources, most building-related waste can be reduced [14,25,26]. Therefore, the identification of waste-generating sources is critical for properly managing construction material waste to ensure the conservation of natural resources and to reduce the cost and impacts of waste.

2.2. Causes of Material Waste Generation

There may be numerous causes for the generation of construction waste in different systems. However, some general causes of waste generation have been identified at different stages of the construction process [1]. A study by [13] grouped the causes of construction waste into seven categories, including design, workers, management, procurement, site conditions, handling, and external factors. The findings of several noteworthy studies conducted by various scholars and researchers in this regard are illustrated in summary in

Table 1. List of causes contributing to construction material waste.

Causes	[27]	[28]	[29]	[20]	[30]	[31]	[32]	[21]	[33]	[15]	[24]	[8]	[5]	[15]
Poor site management and supervision	√		√				√					√		√
Inadequate planning and scheduling					√			√	√			√		√
Frequent design changes	√				√	√				√				√
Mistakes and errors in design		√			√					√	√
Incompetent subcontractors	√				√	√			√	√
Less experienced labour			√	√	√	√	√	√						√
Lack of coordination between parties	√					√		√
Effect of weather	√								√	√	√	√
Rework	√					√	√				√	√
Lack of experience of work	√				√			√	√					√
Inadequate monitoring and control									√				√	√
Shortage of technical personnel			√							√				√

.

As shown in Table 1, the lack of experienced labour is one of the common reasons for the generation of waste. In addition, Reference [24] emphasized reasons for material waste during the operational stage include using untrained/less experienced manpower, equipment malfunctioning, inexperience in use of construction methods, and lack of coordination. The two main reasons behind this are workers’ beliefs that wastage is unavoidable and the lack of supervision and training [31]. In addition, reworking due to the mistakes of construction workers is ranked second for the generation of waste [17]. The authors further stated that these causes are common for site-level waste management and control.

The preceding discussion emphasizes the connection between workforce experience and construction material waste generation. For in-depth analysis of experience and wastage, learning curve theory is applicable and has been tested in several studies [29]. Learning curve theory suggests that there is a correlation between someone’s productivity in completing a task and the number of times they have practised it. Hence, construction workers could generate less waste if they have repeated the same type of construction work as it helps them to specialise in particular tasks, minimising mistakes made. Similar research was conducted by [26] on the application of learning curve theory to the minimisation of material waste generation. According to this study, it was found that, when the experience of workers increased, the amount of material waste generation was gradually reduced. The need for a deeper and broader understanding of this was the point of departure for this research.

The methodology adopted in this research is explained in the next section.

3. Experimental Research Design

This section is divided into subsections. It provides a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that have been drawn.

The methodology is typically concerned with the logic of research inquiry particularly in relation to investigating the potentialities and limitations of certain types of techniques or procedures [34]. The research design consisted of three stages, including the selection of construction sites, quantification of material wastage, and investigation of LE. Site observation and unstructured interviews were undertaken to gather data on material wastage and labour experience, respectively, in a controlled environment to hold other factors constant affecting brick and tile CMW generation other than LE. Thus, the following steps were taken to create a controlled environment:

(1)

Construction sites were selected only from C1 graded (the highest division) contracting organizations and sites chosen were for commercial buildings with 3–5 stories to ensure a similar type of construction.

(2)

Construction work (brickwork and tiling) was observed when under supervision to provide the same context.

(3)

Observations were made in good weather conditions when the temperature was between 25–35 °C with no rainy conditions.

(4)

Observations were made without informing labourers to ensure that labourers’ attitudes to the generation of material waste were unaffected.

The work gangs selected included only one mason/tiler with one helper at selected construction sites to avoid difficulties in ascertaining LE.

3.1. Selection of Construction Sites

Three (3) C1-graded building contraction organizations that had under construction commercial buildings of a similar nature (with 3–5 stories) and had ongoing brick and tiling projects were selected. A study by [35] stated that C1 grading contractors have a proper waste management system. Due to the constant factor of using a waste management system, only C1 contractors were selected for this study. Three projects (sites) that used brick and tile works were selected from each contraction organisation using stratified cluster sampling. In addition, contracting organisations in the Colombo area were selected for the study due to easily accessible data.

As shown in Figure 1, four non-participant direct observations were conducted separately for each brick and tiling works with normally adopted supervision and without informing labourers. Accordingly, thirty-six samples were tested separately for each brick and tile works. The sample size for this research was chosen as thirty-six (36) for two reasons: to reduce sampling error and because of time constraints.

3.2. Quantification of Material Wastage

The material wastage of different labourers can be measured using work studies of observations or material reconciliation methods [26]. Material reconciliation involves a comparison of material at the beginning and at the end to determine usage [36]. The authors concluded that the material reconciliation method was less time-consuming and easy to effect, though it requires proper recording and illicit waste is also included. Walliman [33] stated that observation is one of the most widely applied methods in scientific research and is used to establish relationships among events, locate the causes behind them, investigate the phenomenon, and test hypotheses systematically. Therefore, material wastage was determined by non-participation direct observation during a day for each of the thirty-six samples.

Both granular and off-cut waste generated during the observation period (a day), was collected in a box and the weight of the collected waste material was measured while observing the actual work done. The weight of a randomly selected unit of material was checked for every observation and the quantity of material used during the observation period was recorded. The waste quantity, the weight of actual work done, and the wastage percentage (weight-wise) were quantified based on formulas illustrated in Equation (3) for bricks and tiles separately.

$$\mathrm{Waste} \mathrm{quantity}\left(\mathrm{Kg}\right)=\mathrm{Weight} \mathrm{of} \mathrm{aggregated} \mathrm{material} \mathrm{to} \mathrm{box}$$

(1)

$$\begin{array}{c}\mathrm{Weight} \mathrm{of} \mathrm{actual} \mathrm{work} \mathrm{done}\left(\mathrm{Kg}\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=\mathrm{Average} \mathrm{weight} \mathrm{of} \mathrm{a} \mathrm{uniy} \mathrm{of} \mathrm{material}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\times \mathrm{Number} \mathrm{of} \mathrm{units} \mathrm{of} \mathrm{materials}\hfill \end{array}$$

(2)

$$\mathrm{Wastage}\left(\%\mathrm{in} \mathrm{weight}\right)=\frac{\mathrm{Waste} \mathrm{quantity}\left(\mathrm{Kg}\right)}{\mathrm{Weight} \mathrm{of} \mathrm{actual} \mathrm{work} \mathrm{done}\left(\mathrm{Kg}\right)}\times 100$$

(3)

As illustrated in Figure 1, only four field observations were taken from each site separately for brick and tiling works under observation without the knowledge of labourers or interruption to other construction work. According to the findings of [23], the quality of tiles and bricks has a significant impact on waste quantification. Hence, for this study, bricks and tiles of the same quality were taken into consideration.

3.3. Investigation of Labour Experience

Interviews and questionnaires are two possible methods to identify LE. However, understanding questionnaires and answering may be difficult for labourers at sites. Thus, interviews were used to minimise misunderstanding of questions and to obtain further explanations. Accordingly, LE was determined by conducting unstructured interviews with labourers who contributed to the generation of material waste. The number of years of experience of the construction workers and their positions were taken into consideration to investigate the labour experience. A study by [37] considered the years of labour experience and qualifications as characteristics specific to labour that affected the amount of construction waste produced in the construction sector. However, in Sri Lanka it is difficult to find qualified labourers [8]. Therefore, the qualification of labourers was not taken into consideration to determine the LE. It is a similar situation irrespective of education. Following [21], as age was correlated with other variables in the study, it was not considered for the study. On a similar note, age was correlated with other variables that were already taken into account in the study, such as years of experience and job position. Therefore, age did not provide additional insight beyond what was already captured by these other factors. Hence, the number of years of experience and labourer position only were taken into consideration for this study.

Before conducting the interviews, the aim of the interview was explained to all labourers to mitigate unnecessary hesitations. Interviews were conducted after the quantification of material waste and interview questions were constructed to ascertain the following key information in the Sinhala medium:

• Mason’s/tiler’s total experience in the construction industry in different categories such as;

  ➢

  Experience in small house building

  ➢

  Experience working as sub-contracting labour (SCL)

  ➢

  Experience working as direct labour (DL)

  ➢

  Experience working with that contracting organisation

• Mason’s/tiler’s total experience as a mason/tiler (as a skilled worker)
• Experience as a helper (i.e., as an unskilled worker)

Although total experience of masons and tilers was collected according to different categories, addition of all the categories together was used to represent the total experience, as shown in

Table 2. Collected data from direct observations and interviews.

No.	Brickworks									Tile Works
	Average Weight of a Unit of Material (Kg)	Number of Units of Material	Weight of Actual Work Done (Kg)	Waste Quantity (Kg)	Wastage (% in Weight)	Experience of Workers (years)				Average Weight of a Unit of Material (Kg)	Number of Units of Material	Weight of Actual Work Done (Kg)	Waste Quantity (Kg)	Wastage (% in Weight)	Experience of Workers (years)
						Mason (Total)	Helper	Gang	Mason (SL)						Tiler (Total)	Helper	Gang	Tiler (SL)
S1.O1.	2.152	322	693	48	6.93	12	4	16	11	2.257	19	21.44	1.15	5.36	12	6	18	8
S1.O2.	2.144	307	658	43	6.53	6	2	8	2	2.233	18	20.10	1.43	7.11	8	5	13	6
S1.O3.	2.119	337	714	42	5.88	12	3	15	1	2.297	21	24.12	1.24	5.14	15	6	21	10
S1.O4.	2.113	316	668	41	6.14	8	5	13	6	2.144	20	21.44	1.49	6.95	6	3	9	2
S2.O1.	2.129	339	722	42	5.82	16	1	17	5	2.233	24	26.80	0.78	2.91	19	4	23	12
S2.O2.	2.137	342	731	44	6.02	13	8	21	10	2.122	24	25.46	0.84	3.30	21	7	28	15
S2.O3.	2.116	346	732	48	6.56	14	3	17	10	2.165	26	28.14	1.64	5.83	9	4	13	4
S2.O4.	2.189	328	718	43	5.99	18	4	22	15	2.207	17	18.76	1.48	7.89	7	5	12	2
S3.O1.	2.090	321	671	48	7.15	7	2	9	3	2.257	19	21.44	0.88	4.10	14	3	17	10
S3.O2.	2.067	336	694	44	6.34	11	6	17	6	2.278	20	22.78	1.05	4.61	11	7	18	6
S3.O3.	2.009	340	683	59	8.64	6	10	16	7	2.257	19	21.44	1.21	5.64	8	8	16	3
S3.O4.	2.094	297	622	60	9.65	2	5	7	1	2.233	18	20.10	1.36	6.77	7	1	8	5
S4.O1.	1.967	362	712	41	5.76	12	3	15	8	2.233	18	20.10	1.55	7.71	2	4	6	2
S4.O2.	1.981	369	731	38	5.48	19	4	23	16	2.257	19	21.44	1.48	6.90	8	4	12	4
S4.O3.	1.936	361	699	51	7.30	6	2	8	3	2.207	17	18.76	1.67	8.90	5	3	8	2
S4.O4.	1.953	342	668	49	7.34	4	6	10	2	2.278	20	22.78	1.02	4.48	12	5	17	10
S5.O1.	2.017	348	702	45	6.41	8	8	16	6	2.170	21	22.78	0.88	3.86	15	6	21	10
S5.O2.	2.033	359	730	39	5.34	13	2	15	10	2.084	18	18.76	1.59	8.48	4	2	6	1
S5.O3.	2.068	352	728	36	4.95	16	4	20	11	2.116	19	20.10	1.33	6.62	8	1	9	4
S5.O4.	2.030	367	745	36	5.08	21	1	22	19	2.193	22	24.12	1.62	6.72	5	0	5	1
S6.O1.	2.164	324	701	54	7.70	5	4	9	2	2.037	25	25.46	1.43	5.62	9	3	12	3
S6.O2.	2.101	318	668	54	8.08	4	4	8	1	2.010	24	24.12	1.33	5.51	7	4	11	4
S6.O3.	2.089	328	685	49	7.15	9	5	14	5	2.084	27	28.14	1.02	3.62	14	9	23	10
S6.O4.	2.071	324	671	48	7.15	6	2	8	2	2.062	26	26.80	0.98	3.66	13	5	18	11
S7.O1.	2.117	341	722	36	4.99	16	4	20	11	2.184	27	29.48	0.89	3.02	18	5	23	14
S7.O2.	2.117	326	690	46	6.67	6	3	9	4	2.165	26	28.14	1.02	3.62	16	1	17	10
S7.O3.	2.141	312	666	48	7.21	3	1	4	1	2.144	20	21.44	1.55	7.23	5	2	7	2
S7.O4.	2.155	316	681	46	6.75	8	6	14	6	2.084	18	18.76	1.45	7.73	9	4	13	4
S8.O1.	1.983	352	698	41	5.87	11	5	16	5	2.207	17	18.76	1.38	7.36	7	4	11	2
S8.O2.	1.981	371	735	40	5.44	14	6	20	9	2.214	23	25.46	0.66	2.59	22	1	23	18
S8.O3.	1.981	370	733	36	4.91	18	4	22	15	2.233	24	26.80	0.78	2.91	18	6	24	15
S8.O4.	1.971	381	751	33	4.61	24	8	32	20	2.214	23	25.46	1.10	4.32	12	8	20	9
S9.O1.	2.132	318	678	51	7.52	6	2	8	3	1.914	21	20.10	1.48	7.36	8	2	10	4
S9.O2.	2.143	321	688	48	6.98	9	8	17	5	1.949	22	21.44	1.56	7.28	7	5	12	3
S9.O3.	2.133	339	723	40	5.53	11	3	14	10	1.965	30	29.48	1.08	3.66	18	5	23	14
S9.O4.	2.137	336	718	43	5.99	10	4	14	7	1.940	29	28.14	1.22	4.34	12	9	21	6

S1.O1: Site No. 01 Observation No. 01.; SL: Skilled Labour.

.

3.4. Data Analysis Techniques

The ultimate goal of this study was to develop a model to predict average brick and tile CMW generation in relation to different levels of LE. Thus, statistical analysis tools, including the simple arithmetic mean, scatter diagrams, and regression were used to analyse the collected data.

3.4.1. Simple Arithmetic Mean Value

Popularly known as the average, the simple arithmetic mean is the most important and frequently used measure of central tendency [38]. The simple arithmetic mean was used to determine average brick and tile CMW during the operational stage based on Equation (4).

$$Simple arithmetric mean=\frac{\sum x}{n}$$

(4)

x—Material wastage

n—Number of observations

3.4.2. Scatter Diagram

“Scatter diagram reveals whether the general course of movement of paired points is best explained by a straight line or a curve” [38]. A scatter diagram was plotted using collected data for LE and brick and tile CMW with the use of the Microsoft Excel application; it was used to help to ascertain whether there was a correlation between brick and tile CMW and LE.

3.4.3. Regression Model

“Regression is mainly concerned with bringing out the nature of the relationship and using it to know the best approximate value of one variable corresponding to the known value of the other variable” [38]. After the identification of the type of correlation from the scatter diagram, a regression model was developed between LE and brick and tile CMW using the Microsoft Excel application. Three regression models, listed below, were developed and the most appropriate model was selected to predict average brick and tile CMW according to total experience, masons’/tillers’ experience, and gang experience.

Second-order polynomial model

$$y=a{x}^2+bx+c$$

(5)

Logarithmic model

$$y=d*\left(In\right)x+e$$

(6)

Power model

$$y=f{x}^{-m}$$

(7)

a, b, c, d, e, f, m are arbitrary constants.

Regression was used to develop a model showing the relationship between the LE and material wastage to predict the average wastage in relation to certain experiences. The regression models were developed for two types of experience level for each material. One type was based on total mason’s/tiler’s experience and the other type was for gang experience. For one material and one type of experience, three regression models were available; these three models were compared, and the best model was selected to predict average material wastage in relation to experience.

4. Research Findings

The weight of a unit material, several unit materials used during the observation period, and the weight of the waste material collected in the box during a specified period, were recorded for all 36 observations. Further, the experience of each mason/tiler and helper were extracted from the interview transcripts. The quantity of materials used and the percentage of material waste was calculated based on Equation (3) given above, while gang experience was calculated by adding total mason/tiler experience to helper experience. The aforementioned data gathered for all nine sites, for both bricks and tiles, are illustrated in Table 2.

The data collected on experience were categorized by experience levels in terms of 3-year slabs since this enabled comparison of the influence of LE on brick and tile CMW. The average percentage of the respective wastages (brick and tile wastages) was calculated: the sum of the percentage of brick/ tile wastage of the LE category was divided by the percentages of the same LE category. The average brick and tile wastage for different LE levels is presented in

Table 3. Average brick and tile wastage (%) against different LE levels.

Total Experience of Workers (years)	B1	B2	B3	T1	T2	T3
Less than 4	8.43	7.41	-	7.71	7.25	-
04–06	7.44	6.46	7.21	7.65	6.28	7.63
07–09	6.79	6.46	7.53	6.74	4.84	7.29
10–12	5.90	5.76	7.13	4.62	3.93	6.85
13–15	5.84	4.91	6.07	4.08	3.22	5.99
16–18	5.33	5.48	6.66	3.30	2.59	4.50
19–21	5.20	5.23	5.35	3.10	-	4.26
More than 21	4.61	-	5.06	2.59	-	3.06

B1: Average percentage of brick wastage according to mason’s total experience; B2: Average percentage of brick wastage according to experience as a mason (SL); B3: Average percentage of brick wastage according to the gang’s experience; T1: Average percentage of tile wastage according to the tiler’s total experience; T2: Average percentage of tile wastage according to experience as a tiler (SL); T3: Average percentage of tile wastage according to the gang’s experience.

.

Table 3 demonstrates that the percentages of average brick wastage varied from 8.43% to 4.61% and the percentages of tile wastage from 7.71% to 2.59%, on an increasing curve of total mason’s or tiler’s experience When considering mason’s and tiler’s experience as skilled labour, the percentage of brick and tile wastage varied from 7.41% to 5.23% and from 7.25% to 2.59%, respectively. The percentage of brick and tile wastage varied from 7.21% to 5.06% and from 7.63% to 3.06%, respectively, with increment in the total labour gang’s experience.

The average brick/tile wastage and the different experience levels presented in Table 3 were entered into the Microsoft Excel application and scatter diagrams were plotted, as shown in Figure 2.

In Figure 2, it is noticeable that, although there were slight fluctuations, when labour experience increased (as a mason or tiler/or total gang), material wastage declined. Further, the Figure illustrates that more experienced skilled labour (masons or tilers) and labour gangs contributed less to the generation of brick and tile CMW. It demonstrates that the average material waste of a labour gang with a certain level of experience was higher than the average material waste for gang with a skilled labourer’s level of experience (as a total or a mason/ tiler). This demonstrated that skilled labour experience is critical for minimising the generation of brick and tile CMW at the operational stage. In 2004, a study by [39] indicated that the average wastage of bricks and tiles was, respectively, 14% and 10% in Sri Lanka. Using the simple arithmetic mean in this study, the average percentage of brick waste was 6.42% and of tile waste was 5.53% during the construction process. Therefore, it can be inferred that a 50% decrease in brick and tile wastage has occurred during the construction process in this period.

Awareness of how material wastage varies according to the level of labour experience is useful for the appropriate organisation of labour groups, for realistic pricing, and to minimize material wastage. Using the Microsoft Excel application, three regression models were developed for two different experience levels for each material based on collected data, as illustrated in Figure 3 and Figure 4, respectively.

As stressed by [21], in the construction industry, zero waste is not possible for any type of project. Accordingly, for the predicted regression models, the second-order polynomial model and logarithmic models indicate negative and zero wastage regarding certain experience levels, though negative or zero wastage is unlikely to occur in practice in the construction industry. The power regression model does not give negative or zero wastages at any experience level since this function always gives positive values for the dependent variable (average material wastage) for any value of the independent variable (labour experience level). In practice, the dependent variable always takes positive values. This function approaches zero value when the independent variable reaches infinity. Therefore, a power regression model was selected as the most reliable option to predict average brick and tile CMW in relation to the level of experience. The power regression model was selected for this study as the analysed data follow a power-law distribution [40,41]. Similarly, this study investigates the relationship between labour experience and its impact on construction waste generation. Therefore, this study adopted a power regression model. The outcomes are illustrated in Figure 5.

Accordingly, the power model y = 10.48x^−0.23 and y = 13.72x^−0.47 can be used to predict average brick and tile wastage, respectively, in relation to masons’/tiler’s level of experience. The average brick and tile wastage are predicted in relation to gang experience by the power models y = 11.72x^−0.24 and y = 22.25x^−0.56, respectively.

5. Discussion

Construction materials account for the largest input into construction activities in the range of 50–60% of the total cost and the value of material wastage may be significant due to the consumption of materials [2]. On a similar note, as per the experimental values of this study, the percentage of brick waste varied between 9.65–4.39% and tile waste varied between 8.90–2.59%. These values represent materials wasted during the construction process. In the literature, twelve general causes and fourteen specific causes have been suggested for construction waste where human resource is a factor that can affect the generation of CMW [21,23,29,31]. On a similar note, of 14 previous studies, 6 highlighted that the use of fewer experienced labourers had a significant impact on the generation of construction material waste (see Table 1). The research findings revealed that, when considering the labour experience in brickwork, skilled labour experience was 2–24 years and in tiling work was 2–22 years, while unskilled labour experience in brickwork was 1–10 years and in tiling work was 0–9 years. Total labour gang experience was distributed between 4–32 years in brickworks and 5–28 years in tiling works. Accordingly, it was clear that skilled labour experience affected material waste more than unskilled labour. Similarly, the research findings revealed that more experienced skilled labour (masons or tilers) and labour gangs contributed less to the generation of brick and tile CMW. This demonstrates that the average material waste of a labour gang with a certain level of experience is higher than the average material waste of a gang with a skilled labourer’s level of experience (as a total or a mason/tiler). It shows that skilled labour experience is critical for minimizing the generation of brick and tile CMW at the construction stage. According to [42], robots can place materials, such as concrete or bricks, with a higher level of precision and accuracy than humans. This can lead to less material waste due to errors or mistakes in placement. Robots can optimize the use of materials by measuring and cutting them with high accuracy and reducing the amount of waste generated from cutting errors or excess use. However, the use of robotics in construction is new for the Sri Lankan construction industry and the initial costs are very high. Jain [6] describes seven barriers to the widespread adoption of waste management. Lack of experienced labour in the industry is one of the barriers. It has also been shown that CW generation is most impacted by worker errors that require reworking, the non-use of experienced labour and subcontractors, and, especially, worker errors that resulted in this [8,10]. In addition, a lack of basic competence will result in both physical as well as non-physical waste when tasks are carried out.

Moreover, several studies have been conducted to identify the influence of workforce attitudes, design changes, procurement, and labour arrangements in the generation of construction waste. However, there has been little research conducted to determine the influence of labour experience (LE) in the generation of CMW. Hence, this paper provides insights into the influence of labour experience in the generation of construction material waste.

6. Conclusions

Rapid growth in construction activities has increased construction waste and creates massive crises all around the world. Construction waste can be defined as the difference between the value of materials delivered to the site and that used appropriately. Many scholars have emphasised that waste considerably affects the success of any construction project. Indeed, responsiveness to construction wastes is of immense importance to achieving the successful completion of construction projects. Therefore, to minimise waste generation, the root causes of the generation of construction waste need to be addressed. This study identified labour experience as one major cause that affects the generation of brick and tile CMW. According to data tabulation and scatter diagrams constructed focusing on bricks and tiles, it was obvious that when experience levels increased, material wastage decreased (Figure 2). Moreover, it was found that a skilled labourer’s experience was more critical than a helper’s level of experience for brick and tile CMW generation. According to the values obtained, three regression models were developed for skilled labourers’ experience and gang experience separately for the generation of brick and tile waste (Figure 3 and Figure 4). Out of the three models, a power regression model was selected as the best model (Figure 5).

This study was limited to building constructions due to the high level of construction material wastage where tiles and bricks only were considered as they correspond to two of the most wasted materials in the study context. Economic losses associated with the identified wastes are not included in the study due to time limitations. It is recommended to industry practitioners to give more attention to identifying the most wasted materials during construction, to identify the causes for wastage, and to provide a waste management system to mitigate construction waste as much as possible. Further research can be carried out to assess and investigate construction waste management practices that can be applied to reduce the generation of tile and brick waste in building construction projects, as well as to predict wastage for other materials. Further studies can also be performed to analyse the economic losses associated with construction waste. It was determined through this research study that labour experience is a factor in material waste generation. Therefore, it will be interesting to find out the variation in construction costs due to material waste in relation to labour experience in future studies.