Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance

Abstract

This research predicts residual solvent (α), which is a key component of the performance assessment for a sprayed/chip seal. Solvent evaporation from the bituminous binder film of a sprayed seal is too complex and poorly understood to develop a model in a traditional way. In this study, conventional equations for α were assessed that showed prediction inefficiency (R 2 value as low as 0.82) under different experimental conditions. Accordingly, gene expression programming (GEP), an emerging branch in artificial intelligence, was utilised to resolve these difficulties by developing empirical models for α. The data required for model development was obtained from extensive laboratory tests conducted on bitumen-solvent binder films in this research. Model evaluation results showed an excellent degree of correspondence between predictions and experimental results (R 2 = 0.94). This is the first study to model a key component of sprayed seal performance using GEP. The model is recommended for pre-design purposes or as a tool to determine residual solvent in a sprayed seal when laboratory testing is not feasible, thereby saving time and expenditure.

Application of Artificial Intelligence Technology in Game NPC

“…However, in recent years, with the rise of the game industry, the intelligence of NPC in the game has become more and more important. The behavior of NPC plays a vital role in the interest of players in the game, which can be said to be one of the core technologies in game development [2]. This paper will discuss the application of artificial intelligence technology in game NPC in order to provide useful information for game developers in this field.…”