Cracking in concrete structures compromises the durability and functionality of the structures themselves. Different kinds of self-healing concretes, less or more sophisticated, have been developed in the past ten years to overcome early cracks in structures. An experimental study of a novel self-healing concrete is presented. Bitumen, used as the healing agent, is introduced in fresh fibre-reinforced concrete as the coating of steel fibres. The mechanism exploits induction energy to heat up the steel fibres inside the cracked concrete matrix; the bitumen then melts and finally flows into the cracks, sealing them. The aim of the research is to set up the main parameter affecting the performance of the healing mechanism as well as its efficiency. In order to achieve this goal, the microstructure of healed specimens has been studied through Light Microscope. Mechanical behaviour and permeability of the samples, before and after healing, were also checked. Fiber content is studied in the paper amongst the many parameters affecting the mechanism. Results point out the potential of the proposed self-healing mechanism to contrast early cracking (i.e. due to shrinkage). Presence of a certain amount of fibres bridging the crack highly influenced the healing efficiency, and so a uniform distribution inside the concrete matrix, which was directly related to fibre amount and its optimum concrete matrix. @en

In structural engineering, offshore structures are undoubtedly among the most exposed to the effects of harsh environmental conditions. The external conditions of these semi-immersed systems involve complex combinations of wave and wind loads. The operating conditions are also unique because oil production platforms are subjected to repeated loading and unloading cycles of the extracted material, which continuously alter their mass. These characteristics make the definition of a structural health monitoring (SHM) protocol highly challenging but necessary to avoid environmental disasters. In this regard, this study discusses an SHM method that can be applied to offshore structures under realistic wave and wind loads. This approach combines anomaly detection, frequency domain decomposition, and a cointegration strategy. Two machine learning regression algorithms were tested to define a cointegration relationship: the support vector machine and the relevance vector machine. The effectiveness of the overall method was evaluated on time-domain signals generated from a finite-element model of a fixed steel platform, on which the Davenport and JONSWAP spectra were used to simulate wind and wave forces. The results show that this damage detection strategy is effective in supervising the health conditions in the analyzed scenario.@en

In structural engineering, offshore structures are undoubtedly among the most exposed to the effects of harsh environmental conditions. The external conditions of these semi-immersed systems involve complex combinations of wave and wind loads. The operating conditions are also unique because oil production platforms are subjected to repeated loading and unloading cycles of the extracted material, which continuously alter their mass. These characteristics make the definition of a structural health monitoring (SHM) protocol highly challenging but necessary to avoid environmental disasters. In this regard, this study discusses an SHM method that can be applied to offshore structures under realistic wave and wind loads. This approach combines anomaly detection, frequency domain decomposition, and a cointegration strategy. Two machine learning regression algorithms were tested to define a cointegration relationship: the support vector machine and the relevance vector machine. The effectiveness of the overall method was evaluated on time-domain signals generated from a finite-element model of a fixed steel platform, on which the Davenport and JONSWAP spectra were used to simulate wind and wave forces. The results show that this damage detection strategy is effective in supervising the health conditions in the analyzed scenario.@en