Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)

Box compression strength (BCS) is a critical parameter for assessing the performance of shipping containers during transportation. Traditionally, BCS evaluation relies heavily on physical testing, which is both time-consuming and costly. These limitations have prompted industry to seek more efficient and cost-effective alternatives. This study explores the application of artificial neural networks (ANNs) to estimate BCS at an industry-applicable level. A real-world dataset—covering approximately 90% of the box dimensions commonly used in the industry—was utilized to train a generalized ANN model for BCS prediction. The model achieved a prediction error of approximately 10%. When validated against experimentally measured data from laboratory testing, with single-wall B-flute as a representative, the prediction error was at a much lower level, further demonstrating the model’s reliability. This study offers a novel approach to BCS prediction, providing a cost-effective and scalable alternative to traditional physical testing methods in the packaging industry.