Internal force minimization-based adaptive beam string structure: Numerical analysis and experimental study

Abstract

In this study, a novel adaptive beam string structure (ABSS) designed to cope with external loading through sensing and mechanical actuation was developed. Unlike traditional beam string structures, ABSS further contains the monitoring and controlling systems, which allows it to sense and react to loading in real-time. The active control method for ABSS based on internal force minimization was established by using the strategy search algorithm (SSA) and strategy prediction network (SPN). SSA is a global search algorithm proposed through combining the strengths of genetic algorithm and gradient descent, and SPN is an artificial neural network established by applying the backpropagation learning algorithm. The downscaled model and test scheme of ABSS were designed and deployed to clarify its effectiveness. The numerical simulation and field experiments were conducted, and simulation results agree well with measurements. Through comparative analysis of simulation and test results, the adaption of ABSS to loading and validity of control method were verified, and ABSS possesses good control capability on internal forces, which is reflected in two ways: 1) reducing the beam stress responses; and 2) improving the beam stress distribution. The research methods and conclusions of this paper can provide valuable references for the design, fabrication, and control of adaptive structures.