Modeling of construction and demolition waste management based on the project life cycle in Indonesia

Improper management of construction and demolition waste has massively accumulated waste and has raised concerns over its impact on the environment and natural resources. The aims of this research modeled construction waste management based on the project life cycle, starting from design, procurement, storage, and implementation. The study aims to conduct a more in-depth study related to Construction and Demolition Waste (CDW) management to find the right strategy. The research method was used by identifying variables based on the review literature and compiling questionnaires for 80 construction projects in Indonesia that were randomly selected to be modeled with Partial Least Squares (PLS) tools. Based on specific criteria, existing models and indicators were evaluated against reflective outer models in explanatory research. The final model explains that construction waste has 24 indicators, namely, four at the design stage, eight at the procurement stage, four at the storage stage, and eight at the implementation stage. Based on the initial contextual analysis, the design factors are the main factor causing the waste. However, the final model explains that the construction waste contextually only relates to the implementation stage, but the design and storage stages directly affect the implementation stage. Therefore, strategies in waste management must be carried out, starting with the right design and material storage processes that are by standards to minimize waste at the time of implementation.


Introduction
The horrific impact of Construction and Demolition Waste (CDW) is causing public concern due to the high volume of CDW produced and its inadequate management [1][2].This situation causes serious environmental effects associated with the manufacturing process for new building materials, causing the low recovery rate of products [3].Unsustainable industrial development has also created a large amount of CDW and caused severe environmental and social problems [4][5][6].The adverse impact of CDW is increasingly troubling the community, so the environmental impacts related to construction waste recycling and demolition need attention [1,7] A large amount of unprocessed CDW poses serious problems in some countries, especially in residential, institutional, industrial, or commercial construction areas [8].Therefore, CDW management rules have been introduced to ensure organized collection, storage, transportation, processing, and disposal of waste.It is the responsibility of all stakeholders for waste management [9].Improper management of CDW has resulted in massively accumulating waste and has raised concerns over its impact on the environment and natural resources [10].The continuous increase in construction activities, such as infrastructure projects, commercial buildings, and housing programs in some countries, has IOP Publishing doi:10.1088/1755-1315/1263/1/012063 2 increased CDW by leaps and bounds.Nowadays, the production rate of CDW in some countries has not been managed properly [2,11] Based on research in developed countries such as Australia and UK also confirms and encourages that there are many possibilities for effective management of CDW, however this is ignored by developing countries [12].Therefore, the problems faced in the management of CDW in developing countries, especially in Indonesia, are still lagging behind some developed countries.This problem occurs due to the lack of attention to CDW management, and the absence of a strategy prepared to prevent the occurrence of such waste.Based on the existing problems, it is necessary to conduct a more in-depth study related to CDW management to find the right strategy.

Methods
This study used questionnaires that were distributed to 286 projects of companies in the database from an Indonesian construction services development agency, and about 80 management of projects (28% of respondents) responded.The determination of criteria or variables for the type and cause of CDW in Indonesia can be seen in the Table 1.

Results and Discussions
The preparation of the model is carried out by paying attention to the relationship between variables and their indicators (Table 1) in the outer model relationship (the relationship between the construct and its indicator or question item) after ensuring the validity and reliability of data is satisfied.In addition, modeling also pays attention to the relationship between the construction waste variable (Y) and the factor variables that affect it (Planning-X1, Procurement-X2, Storage-X3, and Implementation-X4) in the inner model relationship (the relationship between constructs or between latent variables).Based on a literature review, the preliminary modeling of CDW in relation to the causative factor is shown in Figure 1.In this initial modeling, the four factors are connected simultaneously with construction waste in the form of an inner model in Partial Least Squares (PLS).The four factors are also linked to each other to find out the relationship of factors in the life cycle of a construction project.Each existing inner model is connected reflectively to each indicator according to the indicators in the questionnaire (outer model).Initial modeling and parameter criteria are evaluated and calculated results on the Partial Least Squares (PLS) menu for CDW models, as shown in Figure 1.Based on the initial modeling (Figure 1), several negative relationships were obtained, such as the implementation factors with CDW (-0.088), the storage factors with CDW (-0.051), and the procurement factors with the implementation factors (-0.157).It shows that the negative relationship in storage factors to CDW is in accordance with the modeling in the regression equation.When viewed contextually, the design factor is also the factor that has the greatest influence on construction waste, which is 0.811, which means that the effect is very large.Some indicators were not meet the criteria of model requirements (negative value), so some indicators had to be eliminated or discarded.
According to the output of the analysis results with the bootstrapping method on an initial model, it can be explained that the R Square value is for all factors except the design (is the initial factor that connects 3 factors in the project life cycle), so there is no R Square value in the design.In Figure 1 obtained the R-Square value for procurement factors of 0.674, storage factors of 0.592, implementation factors of 0.742, and CDW variables of 0.489.Thus, it can be concluded that the R-Square value for procurement factors is 0.674 which means that the variability of procurement factors can be explained by the design factors in the model of 67.4%, included in the strong category.Then, the R Square Adjusted value of the storage factors variable of 0.592 means that the variability of storage factors that can be explained by the procurement factors in the model of 59.2% is included in the quite strong category.Furthermore, the R Square Adjusted value of the implementation factors variable of 0.742 means that the variability of implementation factors that can be explained by the design, procurement, and storage factors in the model of 74.2% is also included in the strong category.In addition, the relationship between CDW and the four factors with an R Square value is 0.489 which means that the four factors have an influence of 48.9% which is included in the category is quite strong.
After several evaluations of the outer and inner models based on Table 2, a model of the exact relationship between construction waste and demolition was obtained as shown in Figure 2. The results of the final model evaluation can be explained that the outer model for construction waste indicators where reinforcing, mortar, and asphalt are not included in construction waste in Indonesia.Based on the observations and interviews, this is because the reinforcing, mortar, and asphalt are quite expensive in value their use is more careful, and the remaining construction waste is used again for other types of work (reused).According to the output of the analysis results with the bootstrapping method on a final model, it can be explained that the R-Square value for procurement factors of 0.549, storage factors of 0.503, implementation factors of 0.656, and CDW variables of 0.221.Thus, it can be concluded that the R-Square value for procurement factors is 0.549 which means that the variability of procurement factors can be explained by the design factors in the model of 54.9%, included in the quite strong category.Then, the R Square Adjusted value of the storage factors variable of 0.503 means that the variability of storage factors that can be explained by the procurement factors in the model of 50.3% is also included in the quite strong category.Furthermore, the R Square Adjusted value of the implementation factors variable of 0.656 means that the variability of implementation factors that can be explained by the design and storage factors in the model of 65.6% is included in the strong category.On the other hand, the relationship between CDW and the implementation factors with an R Square value is 0.221, which means that the implementation factors have an influence of 22.1%, which is included in the category is weak.It means that even though the implementation factors are the factors that have the highest relationship directly to minimize the number of CDW, the relationship between both of them is weak so it needs to control implementation from design and storage factors in the life cycle project to minimize the CDW.
Based on the project life cycle, starting from design, procurement, storage, and implementation are interconnected, the design factors had the greatest potential to minimize waste and had been highlighted in various empirical studies [19][20].However, the factors directly related to construction waste contextually are only implementation factors related to design and storage factors.These results are supported by previous research, which explains that the most influential implementation factor is training for workers in using equipment as efficiently as possible.This needs to be supported by policies that cover the procurement and development of quality standards [1][2] Systematic implementation that can be carried out for resource efficiency and reducing environmental impact by reducing waste generation, minimizing transport impacts, maximizing reuse and recycling by improving the quality of secondary materials, and optimizing the environmental performance of processing methods from the design stage [15], [17]

Conclusion
The results of the analysis of the dominant factors affecting construction and demolition waste show that the design factor is the main factor causing the occurrence of waste.However, based on the results of the contextual analysis, only the implementation factors are directly related to construction waste.The design factors strongly correlate with the procurement process that will deliver good material to storage.The storage factors have a quite strong relation to the implementation process in the project life cycle.Therefore, strategies in CDW management must be carried out starting from the right design without lack of detailed product size used, the good procurement to avoid the poor packaging process of goods, and the control of storage processes with high supervision during the handling/storage process that is in accordance with standards to minimize waste at the time of construction.

Figure 1 .
Figure 1.Evaluation of the initial modeling for construction waste.

Figure 2
Figure 2 Evaluation of the final modeling for construction waste Based on Figure 2, it can be explained that the construction waste final model has eliminated some indicators in Table 1.This final model has 7 out of 10 indicators for the type of CDW (Y3, Y9, and Y10 are eliminated) and 24 out of 31 indicators for the cause of CDW (X1.1, X1.2, X1.3, X1.7, X2.3, X3.1 are eliminated), namely four indicators on the design , eight indicators on the procurement, four indicators on the storage, and eight indicators on the implementation.Furthermore, the relationship between construction waste and disassembly to the factors that affect it in the evaluation with Partial Least Squares (PLS) obtained the relationship concluded that the construction waste management model has met all the requirements of the model, the design factor is the main key that becomes the beginning source related to procurement and implementation.According to the output of the analysis results with the bootstrapping method on a final model, it can be explained that the R-Square value for procurement factors of 0.549, storage factors of 0.503, implementation factors of 0.656, and CDW variables of 0.221.Thus, it can be concluded that the R-

Table 1 .
Research variables (cont.) Before modeling using Partial Least Squares (PLS), the results of the questionnaire were tested for validity and reliability of the data first using statistical analysis with Statistical Product and Service Solutions (SPSS) software.Next, initial modeling is carried out and evaluated gradually to find a final model that meets all criteria in contextual modeling using Partial Least Squares (PLS).The evaluation criteria of final model from Partial Least Squares (PLS) modeling follow the explanatory research parameter in Table2.

Table 2 .
Evaluation, parameter and criteria of the explanatory research.

Table 2 .
Evaluation, parameter and criteria of the explanatory research (cont.).