By combining rotating squares with auxetic properties, we developed a metamaterial transformer capable of realizing metamaterials with tunable functionalities. We investigated the use of a metamaterial transformer-based thermal cloak–concentrator that can change from a cloak to a concentrator when the device configuration is transformed. We established that the proposed dual-functional metamaterial can either thermally protect a region (cloak) or focus heat flux in a small region (concentrator). The dual functionality was verified by finite element simulations and validated by experiments with a specimen composed of copper, epoxy, and rotating squares. This work provides an effective and efficient method for controlling the gradient of heat, in addition to providing a reference for other thermal metamaterials to possess such controllable functionalities by adapting the concept of a metamaterial transformer.

In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures.

When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). Moreover, we propose a novel hybrid algorithm called the divisional model genetic algorithm (DMGA) to improve efficiency. By adding different methods, namely particle swarm optimization (PSO), pattern search (PS) and targeted mutation (TM) in three subpopulations to become “divisions,” each division has unique functionalities. With the collaboration of these three divisions, this method is considerably more efficient in solving multiple benchmark problems compared with other hybrid algorithms. These results reveal the superiority of DMGA in solving structural optimization problems.

Modal parameter monitoring is a widely used structural health monitoring method. However, among other limitations, this method cannot effectively identify slight damage under ambient conditions. This study proposed a novel strain expansion–reduction approach for identifying damage. To verify the feasibility of the proposed method, we numerically and experimentally tested the method using a rigid acrylic frame. The frame was artificially damaged at various depths to reflect various damage scenarios. The increase in the damage index provided an accurate estimation of damage severity. For the case with merely 0.5% damage zone in one slat, the index is increased by 259% of the intact case. When the damage zone was doubled, the index increases significantly by 467% of the intact case, demonstrating excellent sensitivity of the proposed method. To guarantee practical use, the numerical model of the proposed method was applied to an offshore wind turbine jacket substructure and successfully identified multiple damage sites and the damage severity with extremely high (>10) damage index.