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.

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 an emerging “blue economy,” the use of large multi-purpose floating platforms in the open ocean is being considered. Such platforms could possibly support a diversified range of commercial activities including energy generation, aquaculture, seabed mining, transport, tourism, and sea-based laboratories. A Markov decision process (MDP) framework is proposed to deal with operations and maintenance (O&M) issues that are inevitable; challenges arise from the complex stochastic weather conditions that need to be accounted for. Using data as well as contrasting synthetic simulations of relevant weather variables, we demonstrate the robustness/versatility of the MDP model. Two case studies—one involving constant and another involving time-dependent downtime costs—are conducted to demonstrate how the proposed MDP framework incorporates weather patterns from available data and can offer optimal policies for distinct metocean conditions (i.e., temporal variations in the weather). A realistic example that illustrates the implementation of the proposed framework for multiple O&M issues involving salmon net pens and wave energy converters demonstrates how our optimal policies can minimize O&M costs and maximize crew safety almost as if the true future were known for scheduling.

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.

An offshore energy transition, even if only a gradual one, from carbon-emitting fossil fuel extraction to cleaner sources is recommended, if we are to slow the harmful impacts of climate change. The potential for sustainable reuse of decommissioned offshore jacket platforms to support wind turbines is being considered as an attractive proposition in such a transition. To maximize the benefits of such reuse of assets, what is needed is a rational optimization strategy that considers the remaining life of a repurposed platform, associated retrofit and construction costs, and a future period of gross renewable energy generation following installation of the wind turbine. We outline a study that employs a fatigue reliability-based framework, based on the global fatigue approach and Palmgren–Miner’s rule, to aid in such sustainable reuse planning and optimization. The framework proposed identifies an optimized reuse plan that incorporates metocean data analysis, structural analysis, life cycle evaluation, and revenue optimization. We employ a case study and sustainable reuse scenario for a site in the vicinity of Porto (Leixões), Portugal.