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Strains beyond the elastic limit of concrete cause cracks in concrete structures. This study analysed distributed optical fibre sensor (DOFS) network strain data to identify excessive strain readings, locations and strain patterns of four reinforced concrete (RC) beams. The strain variation inside the RC beams was investigated using a concrete damaged plasticity (CDP) based finite element analysis (FEA) model. The measured bottom surface and bottom rebar strain were considered for investigation. A three-point bending arrangement was used for the loading, and an optical backscatter reflectometer was used to measure the strain. By visual inspections, it has been found that the onset of hairline cracks at the locations of the higher strain readings. The maximum load applied was 16 kN and has not shown any yielding in strain reading acquired from bottom rebars. FEA results showed admissible agreement with the measured lower surface strain and lower rebar strain. Therefore, when developing a robust SHM system, a CDP-based FEA simulation is a valuable tool for data mining of strain data. Furthermore, the strain data extracted from CDP models can be used to train artificial intelligence based SHM models.

In recent years, researchers have paid special attention to wall uniformity temperature and improvement in a micro-combustor (MC) for applications as thermos-photovoltaic (TPV) since they have a direct influence on feasibility and efficiency of the required conversion of energy. Numerous experimentation and numerical studies in the field of micro-combustion have been carried out and published in this respect. This study focuses on the flame and flow behaviour in an improved MC. Utilizing the famous approach of fluid dynamics being computational (CFD), the impact of geometrical flow parameters behaviour and temperature of wall is explored and measured. The generated model is being used to evaluate the effects of Hydrogen (H) mass flow rate (MFR) and H/air equivalency ratios on the flame pattern and outer temperature of wall of conventional and enhanced MCs. Moreover, the influence of various MC cross sections (CS), such as square, circular, and flat, on temperature of wall is examined. The findings reveal that when the MFR rises, the wall outside temperature rises as well. The MC with flat cross-sections (CSs) is found to be more effective in terms of power of emitter and efficiency of emitter.

Fibre-reinforced composite materials are extensively used for manufacturing critical engineering components in diverse applications, which demands intelligent and reliable structural health monitoring (SHM) schemes to prevent catastrophic failures associated with composite structures. Composite materials have complex failure mechanisms, and it is essential to employ reliable SHM methods with high accuracy to detect damages at the incipient stage. Although there are several SHM technologies available, no single strategy is impeccable for tackling all damage types due to the incredibly complex failure mechanisms of the composite materials. Machine learning (ML) methods are frequently integrated to compensate for the limitations of the traditional SHM methods. This paper presents the state-of-the-art sensory methods and deep learning (DL) techniques while emphasizing the future directions for the engineering and scientific community interested in developing novel SHM systems for fibre-reinforced polymer composite structures intended for civil, aerospace, automotive, marine, oil and gas exploration industries.

Shape memory polymer (SMP) is a smart material that can respond to external stimuli and recover its permanent shape after being programmed. Researchers have been interested in SMPs for invasive biomedical applications, but there are many opportunities for non-invasive applications. Thus, in this study, a novel hybrid SMP nanocomposite smart plaster (SP) is synthesised for non-invasive orthopaedic fractured bone immobilisation. Due to its considerable structural properties, the SP for this study was synthesised with Bisphenol A epoxy, reinforced with E-glass fibres, its bioinspiration qualities were improved incorporating TiO2 nanoparticles. After that, the SP was preserved for three months under five different conditions. This was done to compare their environmental durability and usability for fractured bone immobilisation by analysing the resulting thermomechanical and shape memory properties. In addition, an Abaqus finite element model was developed and validated which can be used to optimise the design and geometrical parameters of the SP. The SP vitro performance was verified, demonstrating a lower limb leg cylindrical cast in less than 10 min. The SP at 50 °C and two layers of cotton webril produced the optimum results, and the recorded maximum undercast temperature was less than 45 °C, which was within the safe limit for human use. Furthermore, the undercast pressure did not surpass 30.2 ± 5.2 mmHg, indicating that the results are equivalent to other bone immobilisation procedures. Therefore, the synthesised SP showed a promising approach to address existing orthopaedic fractured bone limb immobilisation challenges.