Feasibility study of concrete and brick waste recycling program using system dynamics modelling approach

Australian Journal of Asian Country Studies
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Feasibility Study of Concrete and Brick Waste Recycling Program using System Dynamics Modelling Approach
Dat Tien Doan1* Thanwadee Chinda2
1. Master Student, Sirindhorn International Institute of Technology, Thammasat University, Thailand;
2. Assistant Professor, Sirindhorn International Institute of Technology, Thammasat University, Thailand;
*Email address of corresponding author: doantiendat90@gmail.com
Abstract
In Thailand, many infrastructures have been built, such as building, roads etc. to meet the needs of the rapid development of economy. This, in turn, leads to the higher construction and demolition waste, especially concrete and brick waste, with the lower landfill spaces. Recycling program is therefore needed to properly manage the waste, and avoid the future environmental problems. This paper investigates the feasibility of the concrete and brick waste recycling program in Bangkok, Thailand, using a system dynamics modeling technique. The model consists of two main elements, namely the total costs and the total benefits. Five factors, including the truck costs, the fuel costs to recycling places, the labor costs, the training costs, and the machine costs, are under the total costs element. While the total benefits element consists of four factors, namely the savings in leveling costs, the savings in virgin materials, the savings in landfill charge, and the savings in fuel costs to landfills. The simulation results show that it takes 21 years for the recycling program to worth the investment. The government and construction companies could then use the study results as a guideline to plan for their recycling programs.
Keywords: concrete and brick waste, recycling program, system dynamics modeling, Thailand
1. Introduction
In Thailand, the construction area has increased year by year, leading to the raising in the amount of construction and demolition (C&D) waste in which concrete and brick waste made up the majority, around 91.2 % (Sorpimai, 2008). However, almost such waste ends up at landfills whereas it can be recycled or reused for different purposes, such as for levelling or for replacing sand and gravel in aggregate. Plus, there are only two main landfills, Khampangsan and Phanomsarakham, to handle the total waste originating in Bangkok, accounting for one fourth of the amount of waste in this country. (Chinda et al., 2012a). This tendency may, in turn, lead to the shortage of landfills and negative impacts on the environment in the near future.
Although C&D waste recycling has been researched for a long time, at least from 2001 according to Yuan and Shen (2010), especially in developed countries. However, until now it has still received inconsiderable attention from construction companies in Thailand in general and in Bangkok in particular. And those published papers did not concentrate on economic factor, one of the essential criteria that assists such companies consider whether they should investigate in recycling program or not (Chinda et al., 2012b).
In this paper, the feasibility of the concrete and brick waste recycling program in Bangkok is investigated by using a system dynamics modeling (SD) technique to help construction companies have a better view in this activity. They could then use the study results as a guideline to plan for their recycling programs. By doing this, landfills may receive less waste than they used to be and the environmental pollution can be solved.
2. The development of concrete and brick waste recycling program model
SD, introduced by Forrester (1958), is an efficiency tool that can provide a deep insight of the behavior of a complex system. It can be used to build the model in the real world that describe the interrelationship between variables and create different scenarios that can be happened. Users‘ decision-making progress will be better because they can use this tool to predict the future situations. Therefore, it has been used widely in many studies with various domains, especially in C&D waste in recent years. Hao et al. (2007, 2008 and 2010) adopted SD method for C&D waste management, evaluating the alternative of type in C&D waste recycling center was carried out by Zhao et al. (2011)

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and Karavezyris et al. (2002) used SD method to forecast municipal solid waste.
The model in this paper consists of two main elements, namely the total costs and the total benefits. Five factors, including the truck costs, the fuel costs to recycling places, the labor costs, the training costs, and the machine costs, are under the total costs element. While the total benefits element consists of four factors, namely the savings in leveling costs, the savings in virgin materials, the savings in landfill charge, and the savings in fuel costs to landfills.
2.1 Total costs element
Figure 1 shows the total costs element that is the sum of five different factors; training costs, labor costs, fuel costs to recycling places, truck costs, and machine costs, see (1).
Total costs = Training_Costs_submodel.Training_Costs + Labor_Costs_submodel.Labor_Costs + Fuel_Costs_to_construction_sites_submodel.Fuel_Costs + Truck_Costs_submodel.Truck_Costs + Machine_Costs_submodel.Machine_Costs (1)
Figure 1. The total costs element
2.1.1 Training costs factor
To have a higher productivity in concrete and brick waste sorting activity, new labors who are recruited for this sector will be trained for five days before working. One way to save a large amount of money for this is that trained workers in the first year will train others in the following years. In other word, the costs for training (as shown in Figure 2) are only paid in the first year, see (2).
Training costs = IF Year = 1 THEN New_Sorting_Labors*Cost_per_Labor*(1+Inflation_Rate) ELSE 0 (2)
Figure 2. Training costs factor
2.1.2 Labor costs factor
In this part, the costs are based on the number of labors working in recycling sector, see (3). And the total amount of waste that is sorted (as shown in Figure 3) will help to define the number of workers, see (4). In each year, the total sorted waste is computed by the working productivity of new recruited

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labors and current labors.
Labor costs = IF Year = 0 THEN 0 ELSE (Final_Total_New_Sorting_Labors + Final_Total_Regular_Labors)*Number_of_Working_Days*Wage_per_Labors*(1 + Inflation_Rate)^Year (3)
Final sorted waste = IF Year = 0 THEN 0 ELSE IF Year=1 THEN Sorting_Productivity*(Final_Total_New_Sorting_Labors*(Number_of_Working_Days – Number_of_Training_Days) + Final_Total_Regular_Labors* Number_of_Working_Days) ELSE Sorting_Productivity*( (Final_Total_Regular_Labors + Final_Total_New_Sorting_Labors - Final_Training_Group - Final_New_Sorting_Labors)* Number_of_Working_Days + (Final_Training_Group+Final_New_Sorting_Labors)*(Number_of_Working_Days – Number_of_Training_Days)) (4)
Figure 3. Labor costs factor
2.1.3 Truck costs factor
In order to transport the concrete and brick waste to landfills or recycling places, construction companies need to buy or hire trucks. In this paper, trucks will use natural gas vehicle (NGV) instead of diesel to save the cost for fuel, according to Jaroonrat Engineering company. The truck costs are sum of buying costs and renting costs, see (5). The costs for buying new trucks are calculated based on nine factors; including NGV installation cost, cost for new trucks, big maintenance cost, selling trucks savings, regular maintenance cost, tire cost, insurance cost, driver cost, and route cost, see (6). While six elements are used to define the costs for rent; regular maintenance cost, tire cost, insurance cost, driver cost, rental trucks cost and route cost, see (7).
Truck costs = Buying_Costs + Renting_Costs (5)
Buying costs = NGV_Installation_Cost + Cost_for__New_Trucks + Big_Maintenance_Cost - Selling_Trucks_Saving + (Regular_Maintenance_Cost + Tire_Cost + Insurance_Cost + Driver_Cost + Route_Cost)*Bought_Trucks (6)
Renting costs = (Regular_Maintenance_Cost + Route_Cost + Tire_Cost + Insurance_Cost + Driver_Cost + Rental_Trucks_Cost)*Number_of_Rented_Trucks (7)

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2.1.4 Fuel costs factor
Figure 5 shows the way to determine the fuel costs. They are the product of the fuel cost per kilometer and the distance from construction sites to recycling places, see (8).
Fuel costs = Distance*Fuel_Costs_per_km (8)
Figure 4. Truck costs factor
Figure 5. Fuel costs factor
Figure 6. Machine costs factor
2.1.5 Machine costs factor

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Machines will be bought to crush brick and concrete. The number of machines depends on the total waste that labor sorted (as shown in Figure 6). And its costs are shown on (9).
Machine costs = Number_of_Bought_Machines*Cost_per_Machine*(1+Inflation_rate)^Year (9)
2.2 Total benefits element
Figure 7 shows the total benefits element that is the sum of four factors; including savings in fuel costs to landfill, savings in landfill charge, savings in levelling costs, and savings in virgin materials, see (10).
Total benefits = Savings_in_Fuel_Costs_to_Landfill_submodel.Fuel_Costs Savings_in_Landfill_Charge_submodel.Savings_in_Landfill_Charge Savings_in_Leveling_Costs_submodel.Savings_in_leveling_cost Savings_in_Virgin_Materials_submodel.Savings_in_virgin_materials (10)
Figure 7. The total benefits element
2.2.1 Savings in fuel costs to landfill factor
When the waste recycling program is applied by construction companies, fuel costs are saved (as shown in Figure 8). That is because the average distance from construction sites to recycling places is less than the distance from construction sites to landfills, see (11).
Savings in fuel costs to landfill = Distance*Fuel_Costs_per_km + Number_of_Trucks*(Regular_Maintenance_Cost + Tire_Cost) (11)
Figure 8. Savings in fuel costs to landfill factor

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Figure 9. Savings in landfill charge factor
2.2.2 Savings in landfill charge factor
A large amount of money can be saved from landfill charge because almost concrete and brick waste is reused or recycled rather than being transported to landfills (as shown in Figure 9). (12) shows the way to determine the savings.
Savings in landfill charge = Fuel_Costs_submodel.Reused_and_Recycled_Waste*Landfill_Charge_per_ton*(1 + the_Increasing_Percentage_of_Landfill_Charge)^Year_stock (12)
2.2.3 Savings in levelling costs factor
Concrete and brick waste can be reused directly to replace the role of sand or gravel for some activities such as levelling roads or building (as shown in Figure 10). These savings are affected by the price of material that is replaced by concrete and brick waste, see (13).
Savings in levelling costs = Reused_Waste*Sand_Price*(1 + the_Increasing_Percentage_of_Sand_Price)^Year (13)
Figure 10. Savings in levelling costs factor
2.2.4 Savings in virgin materials factor
New aggregate can be created by recycling concrete and brick waste. Such waste will be crushed by machines to generate standard aggregate (as shown in Figure 11). And the price of aggregate will have an impact on these savings see (14).
Savings in virgin materials = Machine_Costs_submodel.Recycled_Waste* Aggregate_Price*(1 + the_Increasing_Percentage_of_Aggregate_Price)^Year (14)
Figure 11. Savings in virgin materials factor
2.3 Financial statement
After defining the total costs and benefits, the feasibility of the recycling program will be assessed by using net present value (NPV) method (as shown in Figure 12). (15) and (16) help to determine the financial statement and NPV result.

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Financial statement = Total_Benefits-Total_Costs (15)
NPV result = Σ Financial_Statement/(1+Rate_of_Return)^Year (16)
3. Results
This dynamic model is stimulated with the iThink program and all the data input is taken from construction companies in Bangkok. Figure 13, 14, 15 and Table 1, 2, 3 show the results of the study.
Figure 13 indicates that the labor costs make up the majority of the total costs, while the truck costs fluctuate every ten years. That is because the huge amount of concrete and brick waste needs a large number of labors, leading to the highest cost and every ten years, construction companies will buy new trucks to replace the old ones.
In terms of the total benefits (as shown in Figure 14), savings in landfill charge stand at the highest position and they will increase dramatically every five years owing to the raising in the landfill charge. Figure 15 shows that although the total benefits are greater than the total cost in the tenth year, construction companies will get the profit in the twenty first year.
Figure 12. Financial statement
Figure 13. The total costs graphical result
1: Training costs 2: Labor costs 3: Fuel costs to recycling places 4: Truck costs 5: Machine costs
Table 1. Total costs result (Bath)

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Year
Training
costs
Labor costs
Fuel costs to
Recycling places
Truck costs
Machine costs
Initial
1
0.00
374,418.00
0.00
1,455,763,270.50
0.00
17,388,725.82
0.00
365,532,764.22
0.00
5,427,636.98
2
3
4
5
6
7
8
9
10
11
…
20
21
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
…
0.00
0.00
1,521,090,656.66
1,589,282,516.13
1,660,450,535.27
1,734,875,056.44
1,812,434,438.20
1,893,505,623.02
1,978,140,782.57
2,066,569,415.04
2,158,757,459.51
2,255,131,212.83
…
3,029,271,038.16
3,120,627,639.85
18,917,949.42
20,382,411.69
21,953,175.08
23,843,090.89
25,660,921.03
27,839,489.73
29,941,026.09
32,450,230.13
34,877,301.31
37,765,032.87
…
68,473,987,33
72,952,090.33
66,905,469.68
71,824,065.71
76,933,524.17
97,292,191.56
89,860,897.09
97,171,374.54
103,193,054.91
111,377,245.79
91,334,544.37
567,016,512.46
…
103,915,665.29
782,347,275.30
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
8,760,151.15
…
0.00
12,218,711.97
Figure 14. The total benefits graphical result
1: Savings in fuel cots to landfill 2: Savings in landfill charge
3: Savings in levelling costs 4: Savings in virgin materials
Table 2. Total savings result (Bath)
Year
Savings in fuel costs to landfill
Savings in landfill charge
Savings in levelling costs
Savings in virgin materials
Initial
1
0.00
80,573,200,43
0.00
583,425,285.00
0.00
8,968,671.54
0.00
385,423,721.76
2
3
86,361,019.75
91,694,189.47
595,882,110.00
608,598,465.00
13,244,040.66
18,267,724.31
402,767,789.24
420,892,339.75

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4
5
6
7
8
9
10
11
…
20
21
97,352,113,78
104,254,060.14
110,663,481.32
118,444,144.57
125,706,298.03
134,481,682.04
142,711,604.86
152,613,332.19
…
250,209,703.79
263,931,460.73
621,556,515.00
1,047,445,245.00
1,069,671,314.25
1,092,393,596.25
1,115,563,383.00
1,139,230,397.25
1,919,438,743.61
1,960,048,125.34
…
5,841,569,565.31
5,882,460,552.27
24,137,713.41
30,990,584.81
38,926,406.05
48,125,772.42
58,744,687.71
70,985,141.72
85,028,022.06
8,478,319.94
…
102,488,301.05
13,001,200.42
439,832,495.04
459,624,957.32
480,308,080.40
501,921,944.02
524,508,431.05
548,111,310.92
572,776,319.91
769,565,898.39
…
1,143,648,141.83
1,327,902,564.68
Figure 15. Financial statement graphical result
1: Financial statement 2: NPV result
Table 3. Financial statement result (Bath)
Year
Financial statement
NPV result
Initial
1
0.00
-786,095,936.79
0.00
-701,871,372.14
2
3
4
5
6
7
8
9
10
-508,659,116.12
-542,036,305.00
-576,458,397.28
-213,695,491.63
-228,386,974.29
-257,631,030.03
-286,752,063.33
-317,588,359.04
434,985,385.26
-1,107,371,305.27
-1,493,182,040.76
-1,859,531,773.68
-1,980,788,334.58
-2,096,496,283.43
-2,213,035,477.71
-2,328,849,826.51
-2,443,375,372.78
-2,303,321,720.76

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11
…
20
21
22,032,766.54
…
4,136,255,021.20
3,499,150,060.65
-2,296,987,826.51
…
-222.242,142.58
101,437,828.24
4. Conclusion
This paper uses a system dynamics modeling technique to predict whether the concrete and brick waste recycling program worth investing. And the results of the model reveal that landfill charge plays an important role in such program. Therefore, once the government imposes a high landfill charge, construction companies will surely apply recycling program instead of transporting waste to landfill. However, as a financial statement shown above, in the beginning years, companies need a lot of capital so as to pay for equipment and recruit labors, so it is hard for small and medium companies to put such program into practice. Thus, both government and companies should work together to make this program more effective. For example, the government can encourage companies participate in this activity by reducing tax or supporting a part of capital.
There are some limitations in this study. Firstly, all the data is taken from companies in Bangkok, so the results might be different when applying in other areas. Users ought to adjust this model to make it more precise. Secondly, there might be more types of costs and benefits in the real situation to add in the model. For instance, if recycling program was applied by companies, they could gain a benefit from brand image which could help to attract more customers and get more profit.
References
Forrest, J.M. (1958). Industrial dynamics: a major breakthrough for design makers. Harvard Business Review, Vol. 26, pp. 37-66.
Chinda, T., Leewattana, N., and Leeamnuayjaroen, N. (2012a). The study of landfill situations in Thailand. In Proceedings of the 1st Mae Fah Luang University International Conference 2012 (1st MFUIC2012) [CD-ROM], 8 p.
Chinda, T., Pornpromtada, K., Wadhanakul, N., and Chavengbenjaporn, S. (2012b). Investigation of factors influencing construction waste recycling decisions. In Proceedings of the 3rd International Conference on Engineering, Project and Production Management (EPPM2012), 10-11 September 2012, Brighton, United Kingdom, pp. 327-334.
Hao, J. L., Hill, M. J., and Huang, T. (2007). A simulation model using system dynamic method for construction and demolition waste management in Hong Kong. Construction Innovation, Vol. 7, pp. 7- 21.
Hao, J. L., Hill, M. J., and Shen, L. Y. (2008). Managing construction waste on-site through system dynamics modelling: the case of Hong Kong. Engineering, Construction and Architectural Management, 15, pp.103–13.
Hao, J. L., Tam, V. W. Y., Yuan, H. P., Wang, J. Y., Li, J. R. (2010). Dynamic modeling of construction and demolition waste management processes: an empirical study in Shenzhen, China. Engineering, Construction and Architectural Management, 17, pp.476-492.
Jaroonrat Engineering company. Retrieved from http://jrr1.jaroonrat.com/ActionController01.php?action=products/ngvActionE
Karavezyris, V., Timpe, K. P., and Marzi, R. (2002). Application of system dynamics and fuzzy logic to forecasting of municipal solid waste. Mathematics and Computers in Simulation, Vol. 60, pp. 149-58.
Sorpimai, K. (2008). Estimation of Construction and Demolition Waste: Case of Bangkok Metropolitan Region. M.Sc. Thesis, AIT, Thailand.
Yuan, H., & Shen, L.(2011) Trend of the research on construction and demolition waste management. Waste management 31: 670–679.
Zhao, W., Ren, H., Rotter, V. S. (2011). A system dynamics model for evaluating the alternative of type in construction and demolition waste recycling center – The case of Chongqing, China. Resource, Conservation and Recycling, 55, pp.933-944.

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Feasibility study of concrete and brick waste recycling program using system dynamics modelling approach