## 7 Design Applications
### Hybrid Modelling--Genetic Algorithm Regression  
New hybrid physical-data driven modelling schemes have steadily been developed over the last three decades and the use of machine learning (ML) and artificial intelligence (AI) technologies has increasingly been promoted in engineering analyses and design routines. The power of hybrid modelling for engineering problem-solving routines is becoming ostensible for many branches of engineering including structural analyses and design procedures. These modelling and design techniques share the same fundamental propositions, namely faster and more accurate modelling and the ability to dynamically change the rules using new data. The source of engineering data for economical and efficient design is growing broader by the introduction of experimental, field study, and simulation data along with smart fitting and extrapolation power of data-driven technologies. For the design of large infrastructural systems, ML and AI are employed as algorithm-based and data-driven schemes, which can be combined with physical analytical and numerical schemes for better optimization and problem-solving capabilities. For these applications, the hybrid models can be categorically grouped into different groups; namely, auxiliary, augmented, full, and dynamic hybrid models, where different optimization applications can be handled using an appropriate type of a hybrid model. While for auxiliary hybrid models, parameters in the physical/empirical models are function-fitted using ML and AI tools, in the case of augmented hybrid models, physical models are augmented with terms/parts derived by function-fitting features of ML and AI tools. For the full hybrid models, data trends from ML and AI can be used along with physical laws to derive the hybrid model. Finally, in the case of trained (or dynamic) hybrid models, existing hybrid models can be subjected to further ML and AI tools for improvement and updating (based on new data). As these techniques are improving and attracting more research into their effectiveness, the drive towards greater scrutiny and examination of the scheme for wider applications in engineering design routines has been raised. These emerging applications can have a great potential for the future of the design of complex systems and their sub-systems, where it can save time and efforts during the design process along with more predictive power for the response of the final engineering system. On the other hand, different machine learning schemes have been developed as a part of general data-driven models, which can handle data classification and pattern recognition. As machine learning algorithms require accurate and reliable data (a sufficient volume of data) to train and learn, experimental, simulated, and/or mined data should be provided for design purposes. Genetic Algorithm Symbolic Regression (GASR) characterizes a data processing scheme where measured or calculated data can be fitted by a suitable mathematical formula (from different family of functions, operators, and so on) using genetic and/or evolutionary algorithms. The GASR technique has been gradually developed for computer implementation in recent years and some computer tools are already available based on GASR technologies. HeuristicLab [40] is one of the open/source academic software tools that have been developed to deal with a variety of data-driven modelling problems. It is prominently useful for problems where computational simulations are combined with optimization and design features within structural engineering projects. In the work herein, the simulation scenario table was set up to define design graphs based on various vehicle speed and distance between vehicle and gantry for roadside structural design using optimized data. The simulation-based data optimization procedures were performed to find symbolic functions for maximum generated pressure during passage.