Data Sprint #4: Compressive Strength of Concrete

Estimate Compressive Strength of Concrete



459 Submissions


Civil engineering is a professional engineering discipline that deals with the design, construction, and maintenance of the physical and naturally built environment, including public works such as roads, bridges, canals, dams, airports, sewerage systems, pipelines, structural components of buildings, and railways.

Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. Compressive strength or compression strength is the capacity of a material or structure to withstand loads tending to reduce the size, as opposed to which withstands loads tending to elongate. In other words, compressive strength resists being pushed together, whereas tensile strength resists tension (being pulled apart). In the study of strength of materials, tensile strength, compressive strength, and shear strength can be analyzed independently.


The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. Your objective is to build a machine learning model that would help Civil Engineers to estimate the compressive strength of the concrete and they can further take a decision whether the concrete should be used in their current project or not.

Evaluation Criteria

Submissions are evaluated using Root-Mean-Squared-Error (RMSE).

How do we do it? 

Once you generate and submit the target variable predictions on the testing dataset, your submissions will be compared with the true values of the target variable. 

The True or Actual values of the target variable are hidden on the DPhi Practice platform so that we can evaluate your model's performance on testing data. Finally, a Root-Mean-Squared-Error (RMSE) for your model will be generated and displayed.


Start Date: 28th August 2020, 21:00 hours IST / 17:30 hours CET  (please locate your time here)

End Date: 31st August 2020, 21:00 hours IST / 17:30 hours CET (please locate your time here)

Do you like to understand the problem through code?

Don't worry! Understand through code! Here is your getting started code

Problem Setter: Manish KC

About the Data

The dataset has 9 columns which tell you different measurements related to the concrete. 

To load the training data on your notebook, use the below command:

import pandas as pd

concrete_data  = pd.read_csv("" )

Data Description
  • Cement (component 1)(kg in a m3 mixture): Cement   (component 1) -- Kilogram in a meter-cube mixture -- Input Variable
  • Blast Furnace Slag (component 2)(kg in a m3 mixture): Blast Furnace   Slag (component 2) -- kg in a m3 mixture -- Input Variable
  • Fly Ash (component 3)(kg in a m3 mixture): Fly Ash   (component 3) -- kg in a m3 mixture -- Input Variable
  • Water  (component 4)(kg in a m3   mixture): Water   (component 4) -- kg in a m3 mixture -- Input Variable
  • Superplasticizer (component 5)(kg in a m3 mixture): Superplasticizer   (component 5) -- kg in a m3 mixture -- Input Variable
  • Coarse Aggregate  (component 6)(kg   in a m3 mixture): Coarse   Aggregate (component 6) -- kg in a m3 mixture -- Input Variable
  • Fine Aggregate (component 7)(kg in a m3 mixture): Fine Aggregate   (component 7) -- kg in a m3 mixture -- Input Variable
  • Age (day): Age -- Day   (1-365) -- Input Variable
  • Concrete compressive strength(MPa, megapascals): Concrete   compressive strength -- MegaPascals -- Output Variable

Test Dataset

Load the test data (name it as test_data). You can load the data using the below command.

test_data = pd.read_csv('')

Here the target column is deliberately not there as you need to predict it.


This dataset has been sourced from the UCI Machine Learning Repository.


You need to choose a submission file.

File Format

Your submission should be in CSV format.


This file should have a header row called 'prediction'.
Please see the instructions to save a prediction file under the “Data” tab.

To participate in this challenge either you have to create a team of atleast None members or join some team