Construction management, price and uncertainty analysis. The intent of the code is to use the Python sampling methods to estimate the uncertainty in the project pricing. Assume a project cost includes the price of material, labour cost, property cost, construction data, and anything else you want to include. However, we know the price, labour cost, construction and other factors will change during construction period. Let's assume that each of the parameters follow the normal distribution with a constant standard deviation (i.e., 10% of the mean) Input(suggest input) (1) Factor that impact the total construction cost (2) Assume input for each factor values (including mean value and standard deviation) Minimum output: (1) Statistic property of the potential total cost (2) What is the probability the cost will exceed 10% or 20% of the expected value (based on the mean) Please use a function in the code (Define a function) for the factors that impact total construction cost so to input different values.
Construction management, price and uncertainty analysis. The intent of the code is to use the Python sampling methods to estimate the uncertainty in the project pricing. Assume a project cost includes the price of material, labour cost, property cost, construction data, and anything else you want to include. However, we know the price, labour cost, construction and other factors will change during construction period. Let's assume that each of the parameters follow the normal distribution with a constant standard deviation (i.e., 10% of the mean) Input(suggest input) (1) Factor that impact the total construction cost (2) Assume input for each factor values (including mean value and standard deviation) Minimum output: (1) Statistic property of the potential total cost (2) What is the probability the cost will exceed 10% or 20% of the expected value (based on the mean) Please use a function in the code (Define a function) for the factors that impact total construction cost so to input different values.
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For the following code can you change the factors that impact the total construction cost section into a function(define a function). Where the mean can be entered through by asking for the inputs of the mean value for each of the factors. (i.e., "Please enter the mean value for material", "Please enter the mean value for labour".. etc.). Follwoing this the standard deviation of the factors is calculated and this is all done in a function.
Transcribed Image Text:Construction management, price and uncertainty analysis. The intent of the code is to use the
Python sampling methods to estimate the uncertainty in the project pricing. Assume a project cost
includes the price of material, labour cost, property cost, construction data, and anything else you
want to include. However, we know the price, labour cost, construction and other factors will
change during construction period. Let's assume that each of the parameters follow the normal
distribution with a constant standard deviation (i.e., 10% of the mean)
Input(suggest input)
(1) Factor that impact the total construction cost
(2) Assume input for each factor values (including mean value and standard deviation)
Minimum output:
(1) Statistic property of the potential total cost
(2) What is the probability the cost will exceed 10% or 20% of the expected value (based on the
mean)
Please use a function in the code (Define a function) for the factors that impact total construction
cost so to input different values.
![import numpy as np
#Defne the factors that impact the total construction cost
factors =["material", "labour", "property", "construction "]
#Define the mean and standard deviation of each factor
means =[100,50,50,10]
stds = [10,5,5,1]
#Sample the values of each factor
samples
=
np.random.normal (means,stds, size=(100, len(factors)))
#Calculate the total cost for each sample
total_costs-np. sum(samples, axis=1)
#Print the statistical properties of the total cost
print("mean:", np.mean(total_costs))
print("standard deviation:",np.std(total_costs))
#Calculate the probability that the total cost will exceed 10% and 20% of the expected value
prob_10=np.mean(total_costs>1.1*np.mean(total_costs))
prob_20=np.mean(total_costs>1.2*np.mean(total_costs))
#Print the probabilities
print("probability of exceeding 10%:", prob_10)
print("probability of exceeding 20% : ", prob_20)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F85fafaa4-de34-47c7-98e7-8408756b8b80%2Ff4db730b-5392-4c86-9914-3813019d653e%2Fxp1z4uf_processed.png&w=3840&q=75)
Transcribed Image Text:import numpy as np
#Defne the factors that impact the total construction cost
factors =["material", "labour", "property", "construction "]
#Define the mean and standard deviation of each factor
means =[100,50,50,10]
stds = [10,5,5,1]
#Sample the values of each factor
samples
=
np.random.normal (means,stds, size=(100, len(factors)))
#Calculate the total cost for each sample
total_costs-np. sum(samples, axis=1)
#Print the statistical properties of the total cost
print("mean:", np.mean(total_costs))
print("standard deviation:",np.std(total_costs))
#Calculate the probability that the total cost will exceed 10% and 20% of the expected value
prob_10=np.mean(total_costs>1.1*np.mean(total_costs))
prob_20=np.mean(total_costs>1.2*np.mean(total_costs))
#Print the probabilities
print("probability of exceeding 10%:", prob_10)
print("probability of exceeding 20% : ", prob_20)
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