Modification Method of Expanded Polystyrene Granular Concrete Based on Computer Data Analysis

Abstract

Expanded Polystyrene Granular concrete (EPS) is renowned for its exceptional lightweight and thermal insulation properties, yet it is hindered by its low strength. Despite the existence of numerous modification methods, there remains a dearth of quantification and summarization, particularly in the era of Artificial Intelligence (AI). Consequently, this study undertook the quantification of several conventional and novel modifications, uncovering intriguing challenges and discerning future trends in modification techniques. These methods encompass nanomaterials, phase change materials, ceramic concrete, and composite modifications, all aimed at bolstering mechanical properties and introducing novel functionalities to address diverse environmental, energy, and engineering requirements. Challenges encompass intricate processes, high costs, material compatibility issues, nascent processes, and application hurdles. Leveraging the advancements in simulation and AI technology, numerical simulations can meticulously replicate the microstructure of EPS granules and the physical attributes of concrete in numerical models, facilitating accurate prediction of material properties. The application of artificial intelligence techniques also includes utilizing big data analysis to optimize the proportions of various materials in EPS granular concrete, providing a valuable framework for the study of high-performance EPS granular concrete and significantly contributing to the enhancement of concrete performance.