University Of Balamand (UOB) Faculty of Engineering Civil Engineering Department PRODUCTION OF CONSTRUCTION OPEARTIONS: AN EXAMPLE ON READY MIX CONCRETE BATCH PLANT By: Dr. Nabil M. Semaan, Ph.D., P.E. FUTURE CONCRETE 2016 MANAGING CONSTRUCTION SITE 25 August 2016, Beirut, Lebanon
2 Dr. Nabil M. Semaan AGENDA PRODUCTIVITY OF CONSTRUCTION OPERATIONS GENERAL CONCEPT FACTORS AFFECTING PRODUCTIVITY APPLICATION TO READY MIX CONCRETE BATCH PLANT DETERMINISTIC PRODUCTION MODEL QUEUYING PRODUCTION MODEL SIMULATION PRODUCTION MODEL
3 Dr. Nabil M. Semaan PRODUCTIVITY OF CONSTRUCTION OPERATIONS Construction Operations: over 1,145,000 business utilize heavy equipment in contract construction. The ability to win contracts and to perform them at a profit is determined by vital assets: Labor and Equipment. Productivity: Helps in evaluating Cost and Time Durations.
4 PRODUCTIVITY OF CONSTRUCTION OPERATIONS Construction Operations: concrete pouring structural steel erection slurry wall construction pile construction Caisson Construction Pipe Laying Brick and Masonry Work Reinforced earth Construction Earthmoving Operations Etc
5 Dr. Nabil M. Semaan PRODUCTIVITY OF CONSTRUCTION OPERATIONS General Concepts: Productivity = Quantity per hour Productivity = No. of Cycles per Hour * Capacity Cost of Production = Equipment Cost per hour / Productivity Time of Production = Total Quantity / Productivity
6 Dr. Nabil M. Semaan PRODUCTIVITY OF CONSTRUCTION OPERATIONS Factors Affecting Productivity: Equipment capacity, power and condition Operator knowledge Ground surface conditions Travel and return distance and speeds Soil classifications, conditions and features Job and management conditions External environmental conditions
7 APPLICATION TO RMC BATCH PLANT A concrete batch plant is a well-developed and industrialized plant, where the concrete is combined before transferring it to the site using transit mixer and ready to be placed. (Utranazz, 2008) Evaluating the production of the RMC batch plant is not straight forward.
8 RMC BATCH PLANT Dry Mobile Transit Mix Wet Mobile Transit Mix Dry Permanent Transit Mix Wet Permanent Central Mix RMC at Construction Site Water added in Transit Truck RMC at Construction Water added in Mixer Fixed Place Water added in Transit Truck Fixed Place Water added in Mixer
9 RMC CENTRAL MIX WET PLANT PROCESS
10 Dr. Nabil M. Semaan DETERMINISTIC PRODUCTION MODEL P BPi = V CM CT BP Where, P BPi is the ideal batch plant productivity (m 3 /hr). V CM is the volume capacity of the central mixer (m 3 ), CT BP is the batch plant Cycle Time (hr.) it takes to: Move the raw material to the central mixer, Mix in the central mixer
11 Dr. Nabil M. Semaan DETERMINISTIC PRODUCTION MODEL P BPa = P BPi n j=1 f j Where, P BPi is the ideal batch plant productivity (m 3 /hr.) P BPa is the actual batch plant productivity (m 3 /hr.), f i are reduction factors, j = 1 to n, n = total number of reduction factors.
12 DETERMINISTIC PRODUCTION MODEL B. P. = P BPa P TR a B.P. is the Balance Point or the required number of transit trucks in order to reach optimum productivity, P BPa is the actual batch plant productivity, P TRa is the actual transit truck productivity
13 DETERMINISTIC PRODUCTION MODEL HOLCIM NAHR EL MAOUT Aggregates (gravel and sand) bins: 3 bins, 45 m 3 capacity each; and 2 bins 48m 3 capacity each. Cement silos: 3 silos, 67 m 3 capacity each. Water tank: 4 tanks, 260 m 3 capacity. Admixtures tanks: 6 tanks, 3 m 3 capacity each. Central mixer: 1 mixer, 2 m 3 theoretical capacity.
14 Task DETERMINISTIC PRODUCTION MODEL Cycle No. Task Starts at Time [sec] Task Duration [sec] Task Ends at Time [sec] Batching Aggregates 1 0 19 19 Discharge Aggregates 1 19 34 53 Batching Cement 1 0 32 32 Discharge Cement 1 47 8 55 Batching Water 1 10 21 31 Discharge Water 1 40 14 54 Batching Admixture 1 0 41 41 Discharge Admixture 1 41 14 55 Mixing Concrete 1 55 30 85 Discharge Mixer 1 85 34 119 119 Batching Aggregates 2 76 20 96 Discharge Aggregates 2 102 36 138 Batching Cement 2 55 32 87 Discharge Cement 2 125 8 133 Batching Water 2 85 21 106 Discharge Water 2 120 14 134 Batching Admixture 2 55 41 96 Discharge Admixture 2 120 14 134 Mixing Concrete 2 138 33 171 Discharge Mixer 2 171 34 205 86 Cycle Time [sec]
15 DETERMINISTIC PRODUCTION MODEL CT TR [min] P TRa [m 3 /hr.] P BPa [m 3 /hr.] BP Required No. of Trucks 15 36 63 1.75 2 20 27 63 2.33 3 30 18 63 3.5 4 45 12 63 5.25 6 60 9 63 7 7 90 6 63 10.5 11 120 4.5 63 14 14
200 Dr. Nabil M. Semaan 16 DETERMINISTIC PRODUCTION MODEL 150 P BPa 100 50 CT=15 CT=20 CT=30 CT=60 CT=90 CT=120 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 No. of Trucks
17 QUEUING PRODUCTION MODEL Service rate: μ= 1/Tservice Arrival rate: λ= 1/Tarrival MARKOV MODEL
18 QUEUING PRODUCTION MODEL Productivity = μ PI Q L μ is the service rate, L is the length of time, PI is the productivity index, Q is the actual capacity of the mixer.
Productivity Index (P.I.) Dr. Nabil M. Semaan 19 QUEUING PRODUCTION MODEL 1.100 1.000 0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 3 4 5 6 7 8 9 No. of Trucks CT=15min CT=20min CT=30min CT=45min
Productivity [m3/hr] 25.0 24.0 23.0 22.0 21.0 20.0 19.0 QUEUING 18.0 PRODUCTION MODEL 17.0 16.0 15.0 14.0 13.0 12.0 11.0 10.0 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Dr. Nabil M. Semaan 10 20 30 40 50 60 70 80 90 100 110 120 Truck Cycle Time [min] Truck No.=3 Truck No.=4 Truck No.=5 Truck No.=6 Truck No.=7 Truck No.=8 Truck No.=9 Truck No.=10 Truck No.=11 Truck No.=12 20
21 SIMULATION PRODUCTION MODEL
22 SIMULATION PRODUCTION MODEL Task SET Probabilistic Duration [sec] FEED AGGR BIN 1 Triangular (42, 53, 64) BLOW CEM SILO 2 Triangular (32, 40, 48) PUMP ADMX TANK 3 Triangular (38, 55, 72) PUMP WATER TANK 4 Triangular (27, 35, 41) MIX CONCR 5 Triangular (21, 30, 39) FILL CONCR TRUCK 6 Triangular (20, 34, 55) TRUCK TRAVEL 7 Triangular (900, 2700, 7200) Concrete Batch Plant Production Productivity Information Total Sim. Time Unit Cycle No. Productivity [Per Time Unit] 98.5 30 0.3044
23 SIMULATION PRODUCTION MODEL
24 SIMULATION PRODUCTION MODEL The stochastic batch plant production (after 30 cycles) is equal to: (0.3045*3600) / 20 [m 3 /sec] = 54.81 m 3 /hr.,
25 SIMULATION PRODUCTION MODEL Type No. Name Average Units Idle Max. Idle Units Times Not Empty % Idle Average Wt. Time Units At End Queue 1 Aggr Avail 0.0 1000 0.0 0.00 0.0 0 Queue 2 Aggr Bin Wt. 1250.0 2250 98.2 99.67 16.3 1250 Queue 4 Cement Avail 0.0 540 0.0 0.00 0.0 0 Queue 5 Cem Silo Wt 1460.0 2000 98.5 99.97 11.1 1460 Queue 7 Admx Avail 970.0 1000 97.7 99.15 1.9 970 Queue 8 Admx Tank Wt 0.0 30 0.0 0.00 0.0 0 Queue 10 Water Avail 700.0 1000 98.5 100.00 19.0 700 Queue 11 Water Tank Wt 0.0 300 0.0 0.00 0.0 0 Queue 13 Aggr Ready 283.3 972 50.1 50.85 5.4 972 Queue 14 Cement Ready 237.9 577 55.8 56.67 10.0 577 Queue 15 Admx Ready 0.5 10 12.9 13.10 1.3 5 Queue 16 Water Ready 278.9 642 71.1 72.16 25.4 642 Queue 17 Mixer Wait 24.2 30 67.0 67.97 27.0 0 Queue 20 Concr Ready 0.6 10 11.9 12.07 0.0 10 Queue 21 Truck Wait 18.3 20 85.5 86.78 39.1 0
26 Dr. Nabil M. Semaan CONCLUSIONS Deterministic Model: Queuing Model: Simulation Model: P BPa = 63 m 3 /hr. P BPa = 23 m 3 /hr. (Max.) P BPa = 54.8 m 3 /hr. (after 30 cycles) Mixer Idle 68% of time 32% efficiency. Simulation Model most comprehensive Points out at bottle necks Queuing Model less accurate lot of assumptions: FIFO Erlang Exponential probability distributions of activities durations
27 THANKS FOR YOUR ATTENTION!