Machine learning technologies for predicting results of cable fire tests

A system for predicting an outcome of a large-scale product test, the system comprising:

• one or more processors;
• a memory storing data associated with a plurality of machine learning models;
• and
• a non-transitory computer-readable memory interfaced with the one or more processors, and storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to:
• receive a set of small-scale results of a product tested according to a small-scale product test representative of the large-scale product test,
• calculate, based on the set of small-scale results as a first input to a first machine learning model of a plurality of machine learning models, a first result predicting an outcome of the product tested according to the large-scale product test, wherein calculating the first result includes:
• determining a first classification for the product, and
• calculating a confidence value for the first classification,
• calculate, based on the set of small-scale results as a second input to at least one second machine learning model of the plurality of machine learning models, a second result predicting the outcome of the product tested according to the large-scale product test, wherein calculating the second result includes:
• determining, based on a test profile for the large-scale product test, a second classification for the product, wherein determining the second classification includes:
• predicting, by the one or more processors and based on the set of small-scale results as the second input to the at least one second machine learning model of the plurality of machine learning models, the test profile, wherein:
• the at least one second machine learning model includes a plurality of regression models, and
• each regression model of the plurality of regression models predicts a test value of a plurality of test values of the test profile, the plurality of test values including at least one of:
• (i) a flame spread value;
• (ii) a total heat release value;
• (iii) a peak heat release rate value;
• and (iv) a fire growth rate index value, and
• predict an outcome of the large-scale product test based at least on the first classification, the confidence value, and the second classification.

A non-transitory computer-readable memory storing one or more instructions for predicting an outcome of a large-scale product test that, when executed by one or more processors, cause the one or more processors to:

• receive a set of small-scale results of a product tested according to a small-scale product test representative of the large-scale product test;
• calculate, based on the set of small-scale results as a first input to a first machine learning model of a plurality of machine learning models, a first result predicting an outcome of the product tested according to the large-scale product test, wherein calculating the first result includes:
• determining a first classification for the product, and
• calculating a confidence value for the first classification;
• calculate, based on the set of small-scale results as a second input to at least one second machine learning model of the plurality of machine learning models, a second result predicting the outcome of the product tested according to the large-scale product test, wherein calculating the second result includes:
• determining, based on a test profile for the large-scale product test, a second classification for the product, wherein determining the second classification includes:
• predicting, by the one or more processors and based on the set of small-scale results as the second input to the at least one second machine learning model of the plurality of machine learning models, the test profile, wherein:
• the at least one second machine learning model includes a plurality of regression models, and
• each regression model of the plurality of regression models predicts a test value of a plurality of test values of the test profile, the plurality of test values including at least one of:
• (i) a flame spread value;
• (ii) a total heat release value;
• (iii) a peak heat release rate value;
• and (iv) a fire growth rate index value;
• and
• predict an outcome of the large-scale product test based at least on the first classification, the confidence value, and the second classification.