cycle timem

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Productivity and delays assessment for concrete batch

plant-truck mixer operations

TAREK M. ZAYED1*, DANIEL W. HALPIN2 and ISMAIL M. BASHA3

1Construction, Engineering and Management Department, Faculty of Engineering, Zagazig University, Zagazig,

Egypt; presently at Department of Building, Civil & Environmental Engineering, Concordia University, 1455 De

maisonneuve west, Montreal, Quebec H3G 1M7, Canada2Construction Division, School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA3

Construction Engineering and Management Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt

Received 24 May 2004; accepted 23 March 2005

Current research focuses on assessing productivity, cost, and delays for concrete batch plant (CBP) operations

using Artificial Neural Network (ANN) methodology. Data were collected to assess cycle time, delays, cost of

delays, cost of delivery, productivity, and price/m3 for the CBP. Two ANN models were designated to represent

the CBP process considering many CBP variables. Input variables include delivery distance, concrete type, and

truck mixers load. Output variables include the assessment of cycle time, cost of delays, delivery cost,

productivity, and price/m3. The ANN outputs have been validated to show the ANNs robustness in assessing

the CBP output variables. The average validity percent for the ANN outputs is 96.25%. A Time-Quantity (TQ)

chart is developed to assess the time required for both truck mixers and the CBP to produce a specified quantity

of concrete. Charts have been developed to predict cycle time/truck, delays/truck, cost of delays/truck, cost of

delivery/m3, and price/m3.

Keywords: Artificial Neural Network (ANN), productivity, cost analysis, cycle time, modeling, concrete batch

plant (CBP), truck mixer

Introduction

Artificial Neural Network (ANN) methodology is

applied to model the concrete batch plant (CBP)

operation and to assess productivity, cost, and delays.

This assessment procedure faces many problems due to

the large number of factors that affect the CBP

operation. Some of these factors can be summarized

as follows: concrete type; delivery distance; transit

mixer capacity; delays in the construction site; delays inthe CBP site; traffic conditions; and road conditions.

Therefore, the price of a concrete unit, which is

produced by the CBP, is affected by the above-

mentioned factors. Common practice prices out the

concrete based upon materials price plus overhead and

profit without considering the transporting distance,

delays in both construction and CBP sites, traffic

conditions, and road conditions. A previous study has

been done by Zayed and Halpin (2001a) to add

distance as a dimension in the concrete unit price. To

enhance the productivity and cost estimation per

concrete unit, current study adds one more dimension

to this previous study to price out the concrete unit:

delays in plant and construction sites. There is a lack of

research in this area; therefore, this study focuses on

assessing the CBP productivity and cost per concrete

unit considering the transporting distance and delays

using ANN.

ANN major concepts

The ANN consists of a large number of artificial

neurons that are arranged into a sequence of layers with

random connections between the layers (Tsoukalas and

Uhrig, 1997). The ANN processing elements (e.g.

neurons) are arranged in layers so that the connections*Author for correspondence. E-mail: [email protected]

Construction Management and Economics (October 2005) 23, 839850

Construction Management and EconomicsISSN 0144-6193 print/ISSN 1466-433X online # 2005 Taylor & Francis

http://www.tandf.co.uk/journals

DOI: 10.1080/01446190500184451


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are systematic and the network can be solved (Portas,

1996). The ANN can be organized through different

layers: input, middle, and output. The middle layer(s)

is (are) called hidden layer(s). These hidden layers have

no connections to the outside world because they are

connected only to the input and output layers. Figure 1

shows a typical ANN structure where weights can beassigned to each connection between two consecutive

neurons.

The ANN performs two major functions: learning

and recalling (Moselhi et al., 1991; Garrett, 1992;

Alsugair and Chang, 1994; Moselhi and Hegazi, 1994;

Tsoukalas and Uhrig, 1997; Bode, 1998). Learning is

the process of adapting the connection weights in an

ANN to produce the desired outputs corresponding to

defined inputs. In other words, the ANN tries to adapt

the weights of network connections so that the network

predicts the required output according to specified

inputs. If the output is provided to the ANN to train

itself, this type of training is calledsupervised learning. Ifthere is no output provided for training, then it is

unsupervised learning. Recall is the process of accepting

an input and producing an output response based on

the ANN weight structure that is trained during the

learning process. In this case, the input vector is

provided to the trained ANN where the predicted

output is calculated after executing the internal process

of the ANN. The predicted output is compared with

the required output to establish the prediction error.

This error is used to adjust the ANN connection

weights to enhance its prediction accuracy.

ANN model building

Fully connected, feed-forward, three-layer, and supervised-learning ANNs have been used in this study for several

reasons (Tsoukalas and Uhrig, 1997): (1) three-layer

ANN is enough to solve all nonlinearly separable

problems; (2) larger number of hidden layers increases

the processing power of the ANN but it needs more

time and data for training process to be performed

properly; (3) one hidden layer (i.e. three-layer ANN) is

capable of representing any mapping; and (4) it is

found from the training process that three-layer ANN is

adequate to represent current problem. Current

research uses three-layer ANN: input, hidden, and

output layers. The input layer includes (as shown in

Figures 2 and 3): (1) delivery distances from the plantto the site: 3, 5, 7, 9, 11, 13 and 15 miles (4.8, 8.0,

11.2, 14.4, 17.6, 20.8, and 24.0 km); (2) concrete types

(2500 psi, 4000 psi, 5.5 bags and 6.0 bags); and (3)

concrete quantity transferred by one truck per trip: 1, 2,

, and 10cy (0.76, 1.52, , and 7.6 m3

). Other

factors that affect the CBP cycle time, productivity, and

cost (i.e. road and traffic conditions) will be studied in

future study(ies) due to data availability. Therefore, the

ANN model includes 12 neurons in the input layer:

Figure 1 Typical feed-forward fully connected artificial neural network structure

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distance (seven variables), concrete types (four vari-

ables), and truck load.

The output layer of the ANN designated model

includes: (1) cycle time elements (loading, hauling,

unloading, and return times); (2) delays (delay inside

the plant and delay in construction site); (3) cost of

delays; (4) price per cy (m3

); and (5) cost of delivery

per cy (m3). Consequently, the number of output

neurons is eight in the output layer. From a practical

standpoint, eight outputs are many for the ANN to

learn through training and to recall for prediction in

future. Lots of information in the output layer results in

large number of neurons in the middle layer(s) to

transfer and translate these information. Therefore,

several iterations have been done to select the best

ANN representation for the CBP operation particularly

the selection of output neurons that make the network

best fit the data. Eight, four, two, and one neurons have

been tried in the output layer. For example, in the case

of one neuron in the output layer, the CBP operation

can be represented by eight networks to cover the eight

output variables; however, four networks represent the

process in the case of two output neurons each. The

outcome of this iteration process leads to the fact that

Figure 2 Cycle time assessment using ANN1

Figure 3 Delays, price, and cost of delivery assessment using ANN2

Productivity and delays assessment 841


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four neurons in the output layer is the best choice to

represent the operation with two networks. The

selection was made based on the learning process

performance. In other words, the iteration of each

ANN training has a goal to achieve, which is the least

sum of square error. If the goal (performance50.05)

has been met, the network learns the data and can

represent the operation. Otherwise, the learning pro-

cess is not efficient and the developed model might

have problems in predicting the outputs. The degree of

goal achievement (performance) is the comparison

indicator that supports the best architecture to achieve

this goal. Using four neurons best meets this goal

because it has a performance of 0.06893, which is close

to 0.05. Other architectures produce larger perfor-

mance (i.e. two output neurons architecture produce

an average performance of 14.6253; however, this is the

second closest to the goal (0.05)). Consequently, the

CBP operation can be represented using two ANNs

(each one contains four output neurons). Details of thisselection process are not included here due to space

limitation.

Each ANNs architecture involves 12 input and four

output neurons. The ANN1 contains loading, hauling,

unloading, and return times in the output neurons. The

ANN2 contains total delays, cost of delay, price per cy

(m3), and cost of delivery/cy (m3) in the output layer

neurons. Figures 2 and 3 show the input and output

neurons in both networks. Both figures depict the

architecture of the networks that represent the CBP

operation. Each network has the same inputs with

different outputs. The hidden layer is different in both

networks because it relies upon the data set and the

nature of the outputs.

Case study

To establish a decision-making framework, a Ready

Mixed Concrete (RMC) batch-plant operation, located

in the Lafayette, Indiana, was studied (Zayed and

Halpin, 2001a). The observed facility consisted of

storage bins for sand and gravel, a hopper tower, two

belt conveyors, two cement silos, and a discharge unit.

This facility serves an area of approximately 15 mi

(24 km) in radius. The production capacity of the plant

was rated as approximately 40cu yd/h (30.4 m3/h)

(Zayed and Halpin, 2001a). Figure 4 shows the flow

diagram for the CBP and the transit mixer cycles.

Materials are withdrawn from the storage area to fill the

batch hopper through conveyor belt 1. There is a

scale to measure the aggregate weight in the hoppertower before discharging to the transit mixer through

conveyor belt 2 (Zayed and Halpin, 2001a). Because

architecture design of the CBP might affect cycle time,

productivity, and cost, it is explained using Figure 4.

A concrete batch plant, in Lafayette, Indiana, is

selected to apply the designated ANN models and

verify their robustness in assessing the delays and their

influence on efficiency, cost, and time. Data (205 data

points) have been collected from the CBP site during

five months period. Several techniques have been used

to collect data: (1) CBP daily reports; (2) interview

with CBP management using site visits and telephone

Figure 4 Flow diagram for the CBP and the transit (adapted from Zayed and Halpin, 2001a)

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calls; and (3) direct data collection forms that are filled

during the CBP site visits. Data have been processed

and analyzed statistically to implement the designated

models. For example, the collected data for output

variables have been normalized (scaled) to reduce the

effect of variables interaction and correlation. Scatter

diagrams, normality plots, and other statistical tests

have been performed to check outliers and abnormal

data points before training the developed ANNs.

The breakdown of concrete price/cy ($/m3) is shown

in Table 1. It shows material, CBP, truck mixer, and

overhead costs. It also shows the total price/cy ($/m3)

for different concrete types based upon 1999 prices.

Cost of delivery/cy (m3) is calculated based upon the

previous study done by Zayed and Halpin (2001a).

Cost of delays/truck cycle is determined using model

(1):

C~ bd1zt d2 =60 1

Current research designed a general framework to

predict cycle time, productivity, cost, and price of the

CBP using the ANN. Because of detailed and

sophisticated data required for this study, the devel-

oped framework is only applied to one concrete batch

plant in Lafayette, Indiana. In other words, intricacy to

collect the required data abridged the available case

studies. Several factors contribute to the obscurity of

generalizing current research results: plant location,

project location, traffic and road conditions, day of the

week, time of the day, inside or outside cities, etc.

Therefore, current research developed a general frame-

work that can be applied to any CBP so that

practitioners can build on and extend to suite their

plants. In other words, practitioners in different cities

can develop an appropriate conversion factor for the

results of current study framework to accommodate

market variations. Reviewing previous work shows lack

of research in the CBP area; therefore, current study

enriches the area of CBP management by developing a

robust ANN model that predicts different management

elements for the CBP. The developed ANN model is

essential for researchers who are engrossed in CBP

management.

ANN models validation

Data were collected and divided randomlyinto two sets:

training and validation data sets. The validation data

set represented 20% of the collected data; however, the

80% left was used to train the ANN models. After

training, the ANN models have been recalled to predict

the outputs based upon the validation data set inputs.

The eight output values are estimated using the

designated ANNs. The resulting outputs are compared

to the collected output data to check the validity

percent of the designated ANNs results. The average

invalidity percent (AIP) value is determined using

model (2) (adapted from Zayed and Halpin, 2001b,

Table 1 Price breakdown per 1 cy (m3

) of concrete

Concrete

type

Material Equipment $/cy ($/m3) Total direct cost Profit & overhead Total price

$/cy ($/m3) Batch plant Truck mixer $/cy ($/m3) $/cy ($/m3) $/cy ($/m3)

2000 psl $32.53 $42.80 $4 ($5.26) $8 ($10.5) $44.53 $58.59 $12.97 $17.07 $57.50 $75.66

2500 psl $34.17 $44.96 $4 ($5.26) $8 ($10.5) $46.17 $60.75 $13.83 $18.19 $60.00 $78.95

3000 psl $36.87 $48.51 $4 ($5.26) $8 ($10.5) $48.87 $64.30 $13.63 $17.94 $62.50 $82.24

3500 psl $38.80 $51.05 $4 ($5.26) $8 ($10.5) $50.80 $66.84 $14.20 $18.69 $65.00 $85.53

4000 psl $40.89 $53.81 $4 ($5.26) $8 ($10.5) $52.89 $69.60 $14.61 $19.22 $67.50 $88.82

4500 psl $43.99 $57.88 $4 ($5.26) $8 ($10.5) $55.99 $73.67 $14.01 $18.44 $70.00 $92.11

5000 psl $43.29 $56.96 $4 ($5.26) $8 ($10.5) $55.29 $72.75 $18.21 $23.96 $73.50 $96.71

4.5 bag

(423 lb)

$40.61 $53.44 $4 ($5.26) $8 ($10.5) $52.61 $69.23 $9.74 $12.81 $62.35 $82.04

5.0 bag(470 lb)

$42.49 $55.91 $4 ($5.26) $8 ($10.5) $54.49 $71.70 $10.36 $13.63 $64.85 $85.33

5.5 bag

(517 lb)

$44.37 $58.39 $4 ($5.26) $8 ($10.5) $56.37 $74.18 $10.98 $14.44 $67.35 $88.62

6.0 bag

(564 lb)

$46.06 $60.60 $4 ($5.26) $8 ($10.5) $58.06 $76.39 $11.79 $15.52 $69.85 $91.91

7.0 bag

(658 lb)

$49.82 $65.55 $4 ($5.26) $8 ($10.5) $61.82 $81.34 $13.03 $17.15 $74.85 $98.49

Sand cost/ton5$10.01; Stone/ton5$15.84; Truck mixer/hr5$80.00; Batch plant/hr5$100.00; Cement/ton5$88.00; Cement/bag5$4.15;Truck/hr510cy (7.6 m

3); Plant/hr540cy (30.4 m3).

Sources: Ogershok and Phillips (1999); RS Means (1999a,b); Ready Mixed Concrete Company (1999).



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CSCE); however, model (3) represents the average

validity percent (AVP) as follows:

AIP~Xni~1

1{ Ei=Ci j j

!,n 2

AVP~1{AIP 3

The AIP values calculated using model (2) are:

delays8.27%; cycle time3.53%; cost of delays

4.56%; cost of delivery2.16%; and price/cy (m3

)

0.23%. Table 2 shows the determination of the average

validity percent (AVP) for the aforementioned output

variables (model 3). It indicates that most of ANN

architectures are excellent in predicting the output

variables. Based upon the analysis of cycle time, it is

clear that the usage of total cycle time as output

variable has a lower AIPvalue than breaking down the

cycle time into its elements. Therefore, breaking down

the cycle time into its elements results in high AIP

values for loading, hauling, and unloading times. The

averageAIP1 (based on total cycle time) is 3.75% that

leads to AVP1 of 96.25%. This percent shows the

robustness of the ANN designated model to predict the

output variables. If the detailed cycle time elements are

used, the average AIP2 (based on cycle time elements)

is 9.00% that leads to AVP2 of 91.00%. Although the

deviation in the cycle time elements is larger than using

the total cycle time, the AVP2 is still acceptable (lower

than 10% deviation). However, it is still essential to

model the cycle time elements to track delays because

they have significant effect on expenses. In conclusion,

the designated ANN models are very convenient torepresent the CBP operation and to predict its delays,

cycle time, cycle time elements, cost of delays, cost of

delivery, and price/cy (m3

). Figures 5 and 6 show the

deviation of the ANN output variable results from the

actual outputs. Cost of delivery/cy (m3) and price/cy

(m3) curves show very minimal deviation; however,

delays and cost of delays show maximum deviation.

Cycle time has a considerable deviation as shown in

Figure 5. There is considerable noise within the cycle

time curve validation data points that range as 713,

2425, and 3133. This noise results from compromis-

ing other factors that affect cycle time, such as concrete

type, traffic, and road conditions.

ANN results analysis

The time required to load a truck mixer as well as the

other cycle time elements and delays are shown in

Figure7. The average loading time for different

delivery distances is 6.25 minutes. In fact, loading time

does not depend on delivery distance; however, its

differences recorded in Figure 7 result from using

average loading time for various concrete types and

round off error. On the other hand, truck mixer

unloading time, which is distance independent, is 10

minutes on average. It depends on the on-site pouringmethod and the number of available spaces beside the

pump. Truck mixer travel and return times are distance

dependent (directly related to distance). In other

words, the higher the distance, the higher the travel

and return times will be. They depend upon traffic and

road conditions but these factors are not covered by the

current study. The curves of cycle time elements are

shown in Figure 7. This figure also shows the average

total cycle time for the various distances and its

associated average delays per cycle. It shows that the

average cycle time, without delays, for a 4.8 km

distance is 26.02 minutes; however, it is 59.99 minutes

for 24.0 km delivery distance. Delays for each truckmixer cycle represent a considerable percent out of the

cycle time: 40.45% on average (17.07 minutes/cycle).

The delay of a truck mixer traveling a 4.8 km distance is

43.97% (11.44 minutes); however, it is almost 35.48%

(21.28 minutes) for traveling 24.0 km distance. These

delays are huge and they have a bad effect on cost per

concrete unit. The total truck mixer cycle time

Table 2 The ANN output validation

Items Output variables AIP1 AVP1 AIP2 AVP2

Delays Delays 8.27% 91.73% 8.27% 91.73%

Cycle time Total 3.53% 96.47% NA NA

Cycle time elements Loading NA NA 23.20% 76.80%

Hauling NA NA 17.06% 82.94%

Unloading NA NA 7.78% 92.22%

Returning NA NA 8.76% 91.24%

Cost of delays Cost of delays 4.56% 95.44% 4.56% 95.44%

Cost of delivery/cy (m3) Cost of delivery/cy (m3) 2.16% 97.84% 2.16% 97.84%

Price/cy (m3) Price/cy (m3) 0.23% 99.77% 0.23% 99.77%

Average Average 3.75% 96.25% 9.00% 91.00%

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Figure 5 The ANN validation using cycle time and delays

Figure 6 The ANN validation using costs and prices



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including delays is determined to be 60.10 minutes on

average; however, it is 37.45 and 81.27 minutes for

delivery distances 4.8 km and 24.0 km, as shown in

Figure 7, respectively.

The effect of cycle time delays appears clearly inFigure8 under the cost of delays category. Figure8

shows the cost of delays per truck cycle for different

delivery distances. It averages $56.15/truck cycle. It

ranges from $37.63 to $70.00/truck cycle for delivery

distances from 4.8 km to 24.0 km, respectively. These

cost figures are averaged for different truck loads and

capacities. Price per cubic meter is also shown in

Figure 8 for various travel distances. It averages $93.85/

m3

for various concrete types, truck loads, and travel

distances. Average price ranges from $81.59 to

$105.83/m3

for distances from 4.8 km to 24.0km,

respectively. This price covers the cost of delivery for

various delivery distances. The cost of delivery estimateshown in Figure8 averages $15.37/m

3for different

travel distances. Its border limits are $5.93 and

$26.38/m3

for 4.8 km and 24.0km travel distances,

respectively.

Based upon the previous analysis for Figures 7 and 8,

the CBP and truck mixer management have to control

their cycle time delays because it will increase the

concrete unit cost by at least $7.39/m3 on average. This

cost increase might exceed the concrete profit margin

resulting in huge losses. It is essential to study the

factors that support this huge delay of 28 minutes per

truck cycle, on average, to stop bleeding money.

Production analysis

A Time-Quantity (TQ) chart, Figure 9, has been

developed to assess time per truck mixer for delivering

a specified quantity of concrete; to determine the CBP

production time for this specified quantity; and to

select the number of trucks required for various travel

distances. The x-axis (horizontal) represents the

required concrete quantities that a client might request.

There are two y-axes (vertical): (1) the left hand side y-

axis represents the time required from one truck mixer

to deliver the required concrete quantity; and (2) the

right hand side y-axis represents the time required fromthe CBP to produce this required concrete quantity.

The CBP production time curve considers the delay

percentages of the truck mixers for various distances.

The TQ chart can be used efficiently when, for

example, a client requested 100 cy (76 m3) of concrete

for a 24 km delivery distance. The CBP management

will use the TQ in Figure 9 to calculate the delivery

time, the CBP production time, and number of truck

mixers that can be used. If a vertical line is constructed

Figure 7 Average cycle time, its elements, and delays

846 Zayedet al.


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from a quantity of 76m3 in the x-axis, Figure 9, it

intersects the CBP production curve and the 24 km

travel distance curves in two points B and A,respectively. From point A, construct a horizontal line

that intersects the left hand side y-axis (time/truck

mixer) in a point (10 hrs) without delays. Therefore, it

takes 10 hours from one truck mixer to deliver this

quantity of concrete. Construct a horizontal line from

point B to the right hand side y-axis (plant production

time). It takes 2.85 hours, including delays, from the

CBP to produce this quantity of concrete. The

optimum number of trucks to be used for this distance

(24 km) is five truck mixers as mentioned in the

keynote in the upper left hand side of the graph

(Figure 9). Therefore, this optimum number of trucks

will keep the CBP busy for 2 hours and 51 minutes(both the CBP and truck mixer) considering the

average delays that might occur for truck mixers cycles.

Cycle time, delays, and expenses analysis

The developed ANN models for the CBP operation are

recalled to predict truck mixer cycle time, delays per

cycle, price/m3

, and the various expenses that are very

essential for the CBP management. Load and unload

times depend on the truck mixers load volume, which

affects the length of truck mixer cycle time. Because thetruck mixer load is one of the controlling factors in the

prediction process, particularly for cycle time, different

truck mixer loads have been used in the prediction

process. The selected truck mixer loads are 5, 6, 7, 8, 9,

and 10 cubic yards (3.8, 4.56, 5.32, 6.08, 6.84, and

7.60 m3). Sets of charts have been developed, based

upon the ANN outputs, to predict the truck mixer cycle

time, delays per cycle, price/m3, and the various

expenses considering the above truck mixers loads

for different types of concrete. This paper presents a

sample of these prediction chart sets that cover only the

4000 psi concrete type.

Figure 10 presents the cycle time prediction for the4000 psi concrete considering different truck mixers

loads. If the truck mixer carries a load of 8 cy (6.08 m3)

for a 11.2 km delivery distance, the corresponding cycle

time is approximately 63 minutes for one truck

including delays. The CBP management can predict

delays considering different distances and truck mixers

loads for the 4000 psi concrete using Figure 11. The

delay for a load of 8 cy (6.08 m3) traveling 11.2 km is

predicted to be approximately 17 minutes. Therefore,

Figure 8 Price and expenses per m3



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Figure 10 Cycle time prediction for 4000 psi concrete

Figure 9 Time-quantity chart for the CBP and truck mixer

Figure 11 Delays prediction for 4000 psi concrete

848 Zayedet al.


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the net cycle time for that distance will be (63 minus

17546 minutes). It is the responsibility of the CBP

management and truck mixers operators to reduce

these delays as much as they can so that they are able to

increase their productivity. The cost of delay for

11.2 km delivery distance can be predicted using

Figure 12. It shows that the cost of delay for an 8 cy

(6.08 m

3

) truck load, 4000 psi concrete, is approxi-mately $43.00/cycle. It costs the CBP management

approximately $7.8/m3 (4000 psi concrete) to deliver to

an 11.2km distance, as shown in Figure13. It is

obvious from Figure13 that the cost of delivery

decreases when the truck mixer load increases because

the truck mixer expenses are allocated to a small

quantity of concrete. On the other hand, Figure 14

shows the prediction of price/m3 for various truck

mixers loads and delivery distances. The price/m3

(including cost of delivery) for 4000 psi concrete that is

delivered for 11.2 km distance is approximately $90.5,

as shown in Figure 14. This shows the drastic increase

in the price/m3

to deliver a small quantity of concrete tothe various distances.

These developed charts (tools) are designated to be

helpful to the CBP management and operators of truck

mixers. They facilitate their decision-making process

particularly in pricing out the concrete unit for different

distances and quantities. The developed tools are

flexible enough to cover many variables that enable

the CBP management to answer a broad range of

questions.

Conclusions

Current research has discussed the role of ANN as a

tool for decision-making and resource management. It

adds one more dimension to the CBP management

analysis: delays and their effect on the price/cy (m3

).

Two ANN models were designated to represent the

Figure 12 Cost of delays prediction per truck mixer cycle

for 4000 psi concrete

Figure 13 Cost of delivery/m3 prediction for 4000 psi

concrete

Figure 14 Price/m3 prediction for 4000 psi concrete



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CBP process considering many input and output

variables. Input variables include various distances,

concrete types, and truckload. Output variables include

the assessment of cycle time, cost of delays, delivery

cost, productivity, and price per concrete unit. The

average validity percent for the ANN outputs is

96.25%, which shows the robustness of the designated

ANN models. A set of charts has been developed to

help the CBP management answer questions regarding

prices and expenses for different distances. The TQ

tool is developed to assess the time required from both

truck mixers and the CBP to produce a specified

quantity of concrete. Charts have been developed to

predict cycle time/truck, delays/truck, cost of delays/

truck, cost of delivery/m3, and price/m3.

This research is relevant to both industry practi-

tioners and researchers. It provides sets of charts for

practitioners usage to schedule and price out the CBP

operation. In addition, it provides the researchers with

the methodology of applying the ANN approach to theCBP operation, its limitations, and future suggestions.

Acknowledgements

The writers wish to express their gratitude and

appreciation to the concrete production companies,

which generously allowed us to collect data and access

their sites. The cooperation and assistance of plants

managers and operators are also gratefully acknowl-

edged and appreciated.

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Appendix

Notations

C5

cost of delays (delays at plant site and atconstruction site)

b5batch plant cost per hour

d15delay at the plant site in minutes

t5truck mixer cost per hour

d25delay at the construction site in minutes

AIP5average invalidity percent for validation data set

AVP5average validity percent for validation data set

Ei5estimated output variables value by ANN for

data point i

Ci5collected output variables value for data point i

n5number of data points

i5data points

850 Zayedet al.


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cycle timem

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