#Civil-Engineering-Machine-Learning
This is Repository for some simple tutorials you can train yourself with on how to get started with AI using Tensorflow
Please Take Note the Tutorial was compiled by: DeveloperPrince for practice purposes
You can utilize the solutions to improve your understanding in AI using TensorFlow
The Solutions are not production ready, if you want to make production ready model please take note of the tensorflow documentation or contact DeveloperPrince
The repository contains two regression based problems one is complete with a saved machine learning model ready for production use and the other is still a work in progress
Concrete compressive strength is determined by mixing different compositions of 7 elements which are namely:
Cement (component 1) Blast Furnace Slag (component 2) Fly Ash (component 3) -- quantitative Water (component 4) -- quantitative Superplasticizer (component 5) Coarse Aggregate (component 6) Fine Aggregate (component 7)
These elements are then allowed to sit for a given time period which will be denoted as:
Age -- quantitative -- Day (1~365) -- Input Variable
For which a load is then applied to dry concrete until it raptures or breaks. The maximum Load the concrete can bear before it breaks is known as the compress strength of the concrete.
in order to make use and test the model run
python Concrete_Comp_Test_Beta.pyThis command will input csv file containing 8 features and labels of data, which will be broken down into testing and training data with a ratio of 2:8
Then it should take in test data as its input then output predictions for the corresponding features.
Inorder to have you own custom input of data run the following
python Concrete_Comp_Test_Beta2.py compile arg1 arg2 arg3 arg4 arg5 arg6 arg7 arg8where the arguments are as follows:
arg1 = Cement quantity arg2 = Blast Furnace Slag quantity arg3 = Fly Ash quantity arg4 = Water quantity arg5 = Superplasticizer quantity arg6 = Coarse Aggregate quantity arg7 = Fine Aggregate quantity arg8 = Age
From the listed arguments you should get you concrete compressive strength as a json object
Here is an example:
python Concrete_Comp_Test_Beta2.py compile 2 5 0 0 45 67 8 85the results:
{"ccst": 577.537109375}If you place more than the required numbers of arguments it will return a json object of error = 1
{"error": 1}If you place less than the required numbers of arguments it will return a json object of error = 0
{"error": 0}Make sure the command for compiling and running the model is compile anything outside this will pass a json object of error = 5
{"error": 5}Make sure the command for compiling and running the model is compile anything outside this will pass a json object of error = 4, this type of error is a runtime error
{"error": 4}Condition monitoring of hydraulic systems is currently a blank or an empty project awaiting for you to try out on building your very own regression machine learning model
Please take note of the following contact details for further assistance
Cellphone/Mobile Number: +263786808538/+263714272770 Email address: princekudzaimaposa94@gmail.com