The beneficial mass-amplification effect induced by the inerter can be conveniently used in enhanced variants of the traditional Tuned Mass Damper (TMD), namely the Tuned Mass-Damper-Inerter (TMDI) and its special case of Tuned Inerter Damper (TID). In this paper, these inerter-based vibration absorbers are studied for mitigating the wind-induced response of high-rise buildings, with particular emphasis on a 340 m tall building analyzed as case study. To adopt a realistic wind-excitation model, the analysis is based on aerodynamic forces computed through experimental wind tunnel tests for a scaled prototype of the benchmark building, which accounts for the actual cross-section of the structure and the existing surrounding conditions. Mass and stiffness parameters are extracted from the finite element model of the primary structure. Performance-based optimization of the TMDI and the TID is carried out to find a good trade-off between displacement- and acceleration-response mitigation, with the installation floor being an explicit design variable in addition to frequency and damping ratio. The results corresponding to 24 different wind directions indicate that the best vibration mitigation is achieved with a lower installation floor of the TMDI/TID scheme than the topmost floor. The effects of different parameters of TMD, TMDI and TID on wind-induced displacement and acceleration responses and on the equivalent static wind loads (ESWLs) are comparatively evaluated. It is shown that the optimally designed TMDI/TID can achieve better wind-induced vibration mitigation than the TMD while allocating lower or null attached mass, especially in terms of acceleration response.

Steel-fibre-reinforced concrete (SFRC) has been increasingly used in the field of engineering structures in recent years. Hence, the accurate monitoring of the fracturing process of in-service SFRC has considerable significance in terms of structural safety. This paper investigates the acoustic emission (AE) and digital image correlation (DIC) features characterising the damage behaviour of SFRC samples in compression. For all the tests, cumulated AE, b-value, β_t coefficient, average frequency, and rise angle are considered to describe the actual SFRC failure mechanisms. The results show that SFRC exhibits enhanced toughness compared to normal concrete (NC), with an indicated transition from a brittle to a ductile structural behaviour. This improved behaviour can be attributed to the bridging effect of steel fibres, which also drives the progressive tensile-to-shear crack transition, thus being the main cause of the final SFRC failure. As the loading rate increases, there is a corresponding increase in the number of shear cracks, leading to a decrease in the overall ductility and toughness of SFRC. Moreover, since the number of shear cracks notably increases right before SFRC fracture, this can serve as a safety warning of the impending failure. Furthermore, the cumulated AE curve displays a strong discontinuity in the occurrence of an unstable fracturing process in SFRC, which can also be forecasted by the AE time-scaling coefficient β_t. The AE and DIC features can be used as failure precursors in the field of structural surveying, offering an accurate technical support for engineering failure warnings.

Since April 2023 an in situ experimental campaign has been underway in a granite underground tunnel, which is a dedicated monitoring platform located in Southeast China. Acoustic emission (AE) signals and seismic sequences were recorded simultaneously by installing the AE device together with the seismometer in order to investigate, among other parameters, the b value and the natural-time variance, κ1, of AE time series. In addition, AE and related temporal correlation with incoming seismic events are analysed using an appropriate multi-modal statistical analysis. The results show that AE has a strong correlation with seismic swarms in surrounding areas. The changing trend in AE temporal distribution occurs before that of the earthquake and regularly anticipates a major seismic event by approximately 17 h. The AE bursts indicate that an earthquake is approaching. The dense clusters of AE are closely related to two major earthquakes with Richter magnitudes equal to 3.2 and 2.4. Approaching the earthquake occurrence, the b value shows a downward trend, reaching its minimum value prior to the earthquake, whereas the natural-time variance κ1 rapidly decreases from 0.07 to a minimum value close to zero. Moreover, κ1 occurs earlier than the minimum b value and the AE bursts. Therefore, trends in the b value and the naturaltime variance derived from the AE time series can be used as effective earthquake precursors. It is also evident that there is widespread micro-seismic activity in the earthquake preparation zone before the earthquake occurrence. The microseismic activity represents the origin of microcracks in the nearby ground surface, resulting in the AE bursts. The results of this paper provide new experimental evidence for the application of fracto-emissions as seismic precursors.

China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F₁ score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning.

Although orthotropic steel decks (OSDs) have been widely used in the construction of long-span bridges, there are frequently reported fatigue cracks after years of operation, and the bridge deck overlay also presents severe damage due to OSD crack-induced stiffness reduction. Ultra-high performance concrete (UHPC), recognized as the most innovative cementitious composites and the next generation of high-performance materials, shows high strength, ductility, toughness, and good performance on durability. After its first application to the OSD bridge in the early 2000s, the orthotropic steel–UHPC composite deck has been comprehensively studied worldwide. This review will summarize some important studies and findings on the behavior and fatigue performance of the orthotropic steel–UHPC composite deck. The existing studies and engineering applications indicate that such a deck system presents good bending behavior and high fatigue performance. The failure mode of shear studs in the UHPC layer is dominated by shear fractures. The cracking of the UHPC layer shall consider the superposition effect of stress from both the whole bridge structure and local decks. While some reasonable structural details in the traditional OSD may not work for the orthotropic steel–UHPC composite deck, this paper has shown that the steel–UHPC composite deck has excellent performance in bearing capacity, stiffness, and fatigue resistance. However, the fatigue performance of the steel–UHPC composite deck and its evaluation method still need validation from engineering applications. It is recommended to evaluate the stress behavior and structural parameters, as well as fatigue life by conducting the field test under in-service traffic conditions.

Compression failure of concrete without effective confinement may show brittle failure modes, and size effects are crucial for understanding catastrophic behaviours. In this study, acoustic emission (AE) and digital image correlation techniques were innovatively combined to investigate the energy release characteristics and crack propagation patterns in concrete specimens under compression. Plain concrete specimens of base size 75, 150 and 300 mm and slenderness ratios of 0.5, 1.0 and 2.0 were tested under compression. The results showed that the compressive strength and ductility of the concrete specimens exhibited significant size and slenderness effects. With an increase in the specimen size or slenderness ratio, a ductile-to-brittle transition occurred and the post-peak behaviour shifted from softening to catastrophic failure, which was accompanied by local and global instability phenomena. This study also highlights the fractal characteristics of AE signals, with a fractal dimension of 2.19, indicating that energy release is primarily concentrated on the surface. This research has significant implications for the structural reliability and safety of concrete structures, reducing the risk of catastrophic failure.