The construction industry is traditionally environmentally unfriendly. The environmental impacts of construction waste include soil contamination, water contamination, and deterioration of landscape. Also, construction waste has a negative economic impact by contributing additional cost to construction due to the need to replace wasted materials. However, in order to mitigate waste, construction managers have to explore management options, which include reduction, recycling, and disposal of wastes. Reduction has the highest priority among the waste management options but efficient reduction cannot be achieved without adequate identification of the sources of waste. Therefore, the purpose of this paper is to present a study that was carried out on the contribution rates of nine identified sources of construction waste. Establishing the contribution rates of different waste sources will enhance knowledge-based decision-making in developing appropriate strategy for mitigating construction waste. Quantitative research method, using survey questionnaire, was adopted in this study to assess the frequency and severity of contribution of the sources of waste. As one of the findings of the study, residual waste such as material off-cuts was identified as the highest contributor to construction waste. This study consequently demonstrated that waste has a significant contribution to the cost of construction.
Waste may be generated during both the extraction and processing of the raw materials and eventual consumption of final products therein. Rubbles and other waste materials arise from construction activities like demolition, renovation of buildings, and new construction [
Construction waste reduction has the highest priority among waste management options which include reduction, recycling, and disposal [
Construction material waste arises from design, logistics, and physical construction processes. In the context of this study, construction wastes are some of the materials delivered to site which have been damaged and meant for disposal, reuse, or recycling. According to Ekanayake and Ofori [
Most previous studies on waste quantification have been focussed on waste segregation for specific materials and the volume of waste generated rather than the impacts of the sources that generated the waste. Bossink and Brouwers [
Following the identification of the sources of construction waste, this study decided to analyse the identified sources in terms of their waste contributions and impacts. In the United States, Gavilan and Bernold [
Sources and causes of construction waste.
Sources of waste | Causes |
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Procurement | Ordering error, supplier’s error resulting in excessive materials on site |
Design | Changes to design, documentation error |
Material handling | Transportation, off-loading, and inappropriate storage |
Operations | Tradesperson’s error, for example, installing wrong materials and having to remove such materials |
Weather | Humidity, temperature |
Vandalism | Inadequate security |
Misplacement | Untraceable materials, abandonment |
Residual | Cutting materials to sizes |
Others | Lack of waste management plan |
In order to have adequate record of waste and then develop tools for waste reduction, there is a need to identify the sources of waste and assess their impacts on project outcomes [
The subsequent contents of this paper are presented as follows. Section
The research methodology adopted in this study was quantitative research method in the form of questionnaire sample survey. Quantitative research is defined as an inquiry into a social or human problem based on testing a hypothesis composed of variables with numbers and analysing with statistical procedure to determine whether the hypothesis holds true [ research design; sampling and data collection.
With sources of construction waste as the independent variables, a survey questionnaire was developed to measure the opinions of building contractors on the severity and frequencies of the contribution of these sources using a Likert scale, which is a multi-item measuring scale where response levels are anchored with consecutive integers and symmetrical about a neutral middle [ severity of contribution: the scale was 1 (none), 2 (little), 3 (moderate), 4 (great), and 5 (extreme); frequency of contribution: the scale was 1 (never), 2 (rarely), 3 (sometimes), 4 (frequently), and 5 (always).
The questionnaire was divided into two parts. The first part sought the background of the respondent, size of their company, catchment area of their projects, annual turnover, and headcount. The second part sought to measure the severity and frequency of waste generation sources. Severity rating is a measure of the extent of the impact of these sources in terms of volume of waste that can be generated while frequency rating is a measure of how often the sources do contribute to construction waste. The product of severity and frequency ratings will produce the significance of the contribution of the sources. An example of a question and response to the questionnaire is shown in Table
Severity of contribution of the sources of construction waste.
Sources of construction waste | |||||
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Sources of waste | Contribution rate | ||||
None | Little | Moderate | Great | Extreme | |
1 | 2 | 3 | 4 | 5 | |
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(a) Procurement such as ordering error and supplier’s error due to inaccurate data | □ | □ |
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□ | □ |
(b) Design such as changes to design and contract document errors | □ | □ | □ |
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□ |
(c) Materials handling such as damage during transportation, off-loading, on-site distribution, and inappropriate storage | □ | □ | □ |
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□ |
(d) Operation such as tradesperson’s error and equipment malfunction | □ | □ |
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□ | □ |
(e) Damage due to weather such as temperature and humidity | □ | □ | □ |
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□ |
(f) Security such as damage on construction site due to vandalism | □ | □ | □ |
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(g) Materials misplacement on site | □ | □ |
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□ | □ |
(h) Residual such as off-cuts from cutting materials to length and packaging | □ | □ | □ |
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□ |
(i) Others such as lack of site materials control and waste management plans | □ | □ |
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□ | □ |
In a questionnaire survey, statistical methods should be used to design a representative sample which will derive findings that are able to reflect the whole population [
Roles and years of experience of respondents.
Roles | Number of responses | Percentage of responses | Years of experience | Number of responses | Percentage of responses |
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Director/senior management | 24 | 47.06 |
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Managers | 14 | 27.45 |
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Others | 13 | 25.49 |
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Totals |
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Totals |
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Classification of respondents’ companies.
Number of responses | Percentage of responses | |
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Work catchment areas | ||
Regional contractors | 16 | 31.37 |
National contractors | 20 | 39.22 |
International contractors | 15 | 29.41 |
Size by annual turnover £M sterling | ||
>1.7 M but ≤8.5 M | 7 | 13.73 |
>8.5 M but ≤43 M | 21 | 41.17 |
>43 M | 23 | 45.10 |
Size by headcount | ||
Up to 10 | 2 | 3.92 |
10–50 | 11 | 21.57 |
50–250 | 15 | 29.41 |
Over 250 | 23 | 45.10 |
The questionnaire responses were analysed using ordinal logistic regression to derive the probabilities of rating categories (1, 2, 3, 4, and 5) for the severity and frequency of the contribution of the sources of construction waste using SPSS software. The probability of a category (e.g., 2) is the number of respondents that chose the category divided by the total number of respondents in the sample. The output of the ordinal logistic regression analysis of the severity of design in contributing to construction waste is highlighted in Table
Ordinal logistic regression analysis result for severity of the sources of waste.
Sources of waste | Probabilities for response categories on severity of sources of waste |
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Category: 1.00 |
Category: 2.00 |
Category: 3.00 |
Category: 4.00 |
Category: 5.00 |
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Data error | 0.000 | 0.275 | 0.451 | 0.196 | 0.078 |
Design |
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Handling | 0.000 | 0.176 | 0.510 | 0.275 | 0.039 |
Operations | 0.000 | 0.176 | 0.706 | 0.118 | 0.000 |
Weather | 0.000 | 0.294 | 0.490 | 0.216 | 0.000 |
Vandalism | 0.000 | 0.196 | 0.627 | 0.176 | 0.000 |
Misplacement | 0.020 | 0.235 | 0.510 | 0.235 | 0.000 |
Residual | 0.000 | 0.216 | 0.392 | 0.294 | 0.098 |
Others | 0.000 | 0.235 | 0.471 | 0.255 | 0.039 |
As demonstrated in Fadiya et al. [
The computed severity and frequency indices of the sources of construction waste are shown in Tables
Severity indices of the sources of construction waste.
Sources | Probabilities for response categories |
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Data error | 0.2745 | 0.4510 | 0.1961 | 0.0784 |
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Design | 0.1569 | 0.5294 | 0.2549 | 0.0588 |
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Handling | 0.1765 | 0.5098 | 0.2745 | 0.0392 |
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Operations | 0.1765 | 0.7059 | 0.1176 | 0.0000 |
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Weather | 0.2941 | 0.4902 | 0.2157 | 0.0000 |
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Vandalism | 0.1961 | 0.6275 | 0.1765 | 0.0000 |
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Misplacement | 0.2353 | 0.5098 | 0.2353 | 0.0000 |
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Residual | 0.2157 | 0.3922 | 0.2941 | 0.0980 |
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Others | 0.2353 | 0.4706 | 0.2549 | 0.0392 |
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Frequency indices of the sources of construction waste.
Sources | Probabilities for response categories |
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Data error | 0.1765 | 0.7059 | 0.1176 | 0.0000 |
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Design | 0.1373 | 0.4118 | 0.4510 | 0.0000 |
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Handling | 0.1176 | 0.5098 | 0.3725 | 0.0000 |
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Operations | 0.1961 | 0.6667 | 0.1373 | 0.0000 |
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Weather | 0.3529 | 0.6471 | 0.0000 | 0.0000 |
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Vandalism | 0.3725 | 0.5882 | 0.0196 | 0.0000 |
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Misplacement | 0.3529 | 0.5882 | 0.0588 | 0.0000 |
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Residual | 0.1765 | 0.4510 | 0.2941 | 0.0784 |
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Others | 0.2157 | 0.6275 | 0.1176 | 0.0000 |
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Contribution indices of the sources of construction waste.
Sources |
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Rates (%) | Ranks |
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Data error | 0.6157 | 0.5882 | 0.3622 | 11.12 | 4 |
Design | 0.6431 | 0.6627 | 0.4262 | 13.08 | 2 |
Handling | 0.6353 | 0.6510 | 0.4136 | 12.69 | 3 |
Operations | 0.5882 | 0.5882 | 0.3460 | 10.62 | 5 |
Weather | 0.5843 | 0.5294 | 0.3093 | 9.494 | 8 |
Vandalism | 0.5961 | 0.5176 | 0.3086 | 9.47 | 9 |
Misplacement | 0.5882 | 0.5412 | 0.3183 | 9.771 | 7 |
Residual | 0.6549 | 0.6549 | 0.4289 | 13.16 | 1 |
Others | 0.6196 | 0.5569 | 0.3450 | 10.59 | 6 |
In addition to the typical values derived in Section
The Chi-Square test was employed in order to analyse the percentages in the population for all the categories (1, 2, 3, 4, and 5). The purpose of the test was to assess the variability of ratings that were assigned to the severity and frequency of the sources of construction waste by the respondents. The null hypothesis was as follows: H0: the percentages of all categories for each source of waste are equal in the underlying population.
The Chi-Square test statistics for both severity and frequency of the sources of construction waste are shown in Table
Chi-Square test statistics for sources of construction waste.
Sources | Severity | Frequency | ||
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Chi-Square | Asymp. Sig. ( |
Chi-Square | Asymp. Sig. ( |
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Data error | 14.961 | 0.002 | 32.118 | 0.000 |
Design | 25.157 | 0.000 | 8.941 | 0.011 |
Handling | 24.059 | 0.000 | 12.118 | 0.002 |
Operations | 32.118 | 0.000 | 25.765 | 0.000 |
Weather | 6.118 | 0.047 | 4.412 | 0.036 |
Vandalism | 19.882 | 0.000 | 48.059 | 0.000 |
Material misplacement | 24.686 | 0.000 | 21.529 | 0.000 |
Residual | 9.471 | 0.024 | 15.745 | 0.001 |
Others | 19.039 | 0.000 | 41.941 | 0.000 |
As described Section
Contribution rates of the sources of construction waste.
The results of this study are corroborated by existing findings. According to Osmani et al. [
The reliability of the results of this study is justified by the results of the Chi-Square test. According to DeCoster and Claypool [
Reliability statistics for the multi-item scales of the sources of construction waste.
Cronbach’s alpha if item deleted (severity) | Cronbach’s alpha if item deleted (frequency) | |
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Data error | 0.772 | 0.612 |
Design | 0.774 | 0.623 |
Handling | 0.757 | 0.593 |
Operation | 0.777 | 0.621 |
Weather | 0.765 | 0.601 |
Vandalism | 0.786 | 0.622 |
Material misplacement | 0.769 | 0.611 |
Residual | 0.818 | 0.626 |
Others | 0.772 | 0.577 |
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Cronbach’s alpha | ||
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Reliability coefficient for severity | 0.797 | |
Reliability coefficient for frequency | 0.638 |
This section is presented to demonstrate the application of the contribution rates derived in this study in estimating the cost of material wastes to construction projects. Although some residual level of construction waste is unavoidable, the correlation between waste and cost minimisation is substantial and provides an incentive for participants in construction projects to pursue them [ The quantity of waste (volume) can be calculated from gross floor area of a building project using SMARTWaste planning tool developed by Building Research Establishment [ The total quantity of materials was 46954.62 m3 and the total cost of materials for the project was £967,453.00. The cost of waste disposal was estimated to be £1037.17 per skip [ Number of skips (see ( Cost of waste disposal (see ( Cost of waste material (see ( Total cost of waste (see (
According to Ekanayake and Ofori [
Typical cost stream of the sources of construction waste.
This study has developed an analytical method of estimating the cost of construction waste. Reliable estimation of the cost of construction waste prior to the commencement of construction activities will help decision makers understand better the cost implication of waste generation and enhance their decision-making in developing the appropriate strategy that can mitigate waste. For example, knowing the extent of contribution and the cost implication of misplacement can help in decision-making on the adoption of information and communication technology- (ICT-) based tracking systems such as radio frequency identification devices (RFID) which can mitigate misplacement and abandonment of materials on large construction sites. Also, consequent upon the findings of this study, waste can be minimised through, for example, design by factoring-in standard dimensions of materials, labour by careful handling of materials during construction, appropriate storage to avoid damage, and so forth.
Furthermore, the findings of this study show that waste is a major contributor to the cost of construction. The total cost of waste is expected to be 30% of the cost of materials. Also, the rates of contribution and corresponding ranking of the sources of waste will enhance prioritisation of the sources that could be mitigated in the face of financial challenges of mitigation strategies. The 1st, 2nd, and 3rd contributors of construction waste are residual (off-cuts of materials to design dimensions), design change, and material handling, respectively. In the future, key project stakeholders can assess the likely volume and cost of waste using the method developed in this study.
The authors declare that there is no conflict of interests regarding the publication of this paper.