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This paper presents an innovative approach to improve the assessment of mechanical responses in short-span bridges, introducing a novel method with significant implications for bridge engineering. The method integrates a convolutional neural network (CNN) and a multilayer perceptron (MLP) model to monitor stiffness degradation in bridge spans over time, representing a significant step forward in SHM techniques. By harnessing the power of neural networks, our approach enables simultaneous monitoring at multiple measurement points across spans or at various time intervals, providing valuable insights into bridge behavior. Through empirical validation, manuscript demonstrates the high accuracy achieved by our combined CNN and MLP model, augmented by power spectral density moments, in evaluating the quality of bridge projects throughout their operational lifespan. Moreover, our method proves highly effective in identifying potential hazardous areas on bridges and detecting structural damage in problematic spans, addressing critical safety concerns in infrastructure management. Furthermore, we propose the integration of data from both non-contact and contact sensors to further enhance the monitoring and assessment of bridge conditions, contributing to the development of more SHM strategies. Additionally, extending the scope of our research to encompass different bridge types and environmental conditions, such as marine environments or high-temperature settings, promises to elucidate the method’s versatility and widespread applicability in practical scenarios. Future directions for research include conducting additional real-world tests on bridge structures to validate the method’s feasibility and accuracy under diverse conditions. In summary, this paper not only presents a cutting-edge methodology for assessing bridge health but also sets the stage for future advancements in structural monitoring technology, with profound implications for the safety and longevity of bridge infrastructure worldwide.

The hazardous areas determination of coal spontaneous combustion plays a vital role in preventing and controlling the coal fire disaster. In this paper, a physical simulation experimental platform involving working face and ground surface air leakage was constructed to study the effects of ventilation rate and air leakage rate on spatial distribution of oxygen concentration in shallow-buried gobs of coal seam group. In addition, based on determined criteria of oxygen concentration, hazardous areas of coal spontaneous combustion were ascertained. Furthermore, the maximum width of hazardous areas was compared and analyzed. The results manifest that hazardous areas of coal spontaneous combustion is the result of the combined action of working face and ground surface air leakage, presenting a coupling form of S-shaped trend and a quarter inverted-cone caused by the working face and ground surface air leakage, respectively. Hazardous areas determination of coal spontaneous combustion using a physical simulation experimental platform can provide experimental foundation and theoretical basis for coal fire preventing and extinguishing in shallow-buried gobs of coal seam group.

Detecting intrusion in hazardous areas is one of the priorities and duties of safety enhancement. With the emergence of vision intelligence technology, hazardous-area-detection algorithms can support safety managers in predicting potential hazards and making decisions. However, because of the dynamic and complex nature of the jobsite, high-risk zones have a different geometry and can be changed following the schedule and workspace of activity. This leads to hazardous areas being annotated manually. Thus, this study proposes a computer vision and a 4D BIM-based approach for intrusion detection in hazardous areas, called IDC4D. IDC4D comprises three modules: (1) the 4D BIM-based safety planning (4BSP) module, which analyzes the hazardous area; (2) the hazardous area registration (HAR) module, which delivers the hazardous area from the BIM model to the camera’s first frame image; and (3) the hazardous-area-intrusion-detection module (HAID), which applies the computer vision algorithm to identify the correlation between workers and hazardous areas. The efficiency of the IDC4D approach is validated by testing a maintenance project on the construction site. IDC4D supports the planner in choosing the plan and detecting the event of workers entering hazardous areas while working. It showed an average precision of 93% and 94% in phase 1 and phase 2, respectively. These findings provide insight into how varying geometries of diverse hazard areas can be handled for enhancing intrusion detection.

(1) Background: Fire affects global agricultural and/or forest ecosystems with high biomass accumulation. However, the delineation of fire-hazardous areas based on satellite-derived active fire intensity is not well-studied. Therefore, examining the characteristics of fire occurrence and development plays an important role in zoning fire-hazardous areas and promoting fire management. (2) Methods: A fire intensity (FI) index was developed with Visible Infrared Imaging Radiometer Suite (VIIRS) active fires and then applied to identify fire-hazardous areas in Northeast China. Combined with terrain, land cover and net primary productivity (NPP), the spatial and temporal characteristics of active fire occurrence were consistently analyzed. Next, a conceptual decision tree model was constructed for delineating fire-induced patterns impacted by varied factors in Northeast China. (3) Results: The accidental, frequent, prone and high-incidence areas of active fires defined by the FI index accounted for 31.62%, 30.97%, 26.23% and 11.18%, respectively. More than 90% of active fires occurred in areas with altitude <350 m above sea level (asl), slope <3° and NPP between 2500–5000 kg·C/m2. Similarly, about 75% occurred in cropland and forest. Then, four fire-induced conceptual patterns driven by different factors were classified, including the agricultural and forest active fire-induced patterns (i.e., the Agri-pattern and FRST pattern) with NPP ranging 2500–5000 kg·C/m2, and two others related to settlements and unused land with an altitude <350 m asl. The Agri-pattern dominates in Northeast China because of agricultural straw burning. (4) Conclusions: Despite the national bans of open burning of straws, active fires due to agricultural production have occurred frequently in Northeast China in the last decade, followed by small and sporadic forest fires. The approach for defining fire-hazardous areas and varied fire occurrence patterns is of significance for fire management and risk prediction at continental to global scales.

A timely understanding of urban expansion in earthquake-prone areas is crucial for earthquake risk assessment and urban planning for earthquake mitigation. However, a comprehensive evaluation of urban expansion in earthquake-prone areas is lacking in China, especially in the context of rapid urbanization. Based on time series urban land data and seismic ground-motion parameter zonation maps, this study analyzed urban expansion in the most seismically hazardous areas (MSHAs) of China from 1992 to 2015 on the national, regional, and city scales. The results show that urban land area in the MSHAs expanded by 6767 km2 from 1992 to 2015, with a gain of 350%. Specifically, the increase in urban land area of small cities in the MSHAs of western China during this period was the fastest, 6.24 times greater than that at the national level. In terms of spatial patterns, the urban land patches in the MSHAs in 2015 were more fragmented than those in 1992 on all scales. The percentage of change in the number of patches and the landscape shape index of the urban land patches of small cities in the MSHAs of western China were the highest across all cities. Therefore, we believe that special attention should be paid to the cities in the MSHAs that exhibit the most rapid increases in both urban land area and fragmentation, especially the small cities in western China. It is imperative to integrate earthquake mitigation into the urban planning of these cities.

From the perspective of risk control, when hydrogen fuel and fuel cells are used on ships, there is a possibility of low-flash fuel leakage, leading to the risk of explosion. Since the fuel cell space (cabin for fuel cell installations) is an enclosed space, any small amount of leakage must be handled properly. In ship design, area classification is a method of analyzing and classifying the areas where explosive gas atmospheres may occur. If the fuel cell space is regarded as a hazardous area, all the electrical devices inside it must be explosion-proof type, which will make the ship’s design very difficult. This paper takes a Chinese fuel cell powered ship as an example to analyze its safety. Firstly, the leakage rates of fuel cell modules, valves, and connectors are calculated. Secondly, the IEC60079-10-1 algorithm is used to calculate the risk level of the fuel cell space. Finally, the ship and fuel cells are optimized and redesigned, and the risk level of the fuel cell space is recalculated and compared. The result shows that the optimized fuel space risk level could be reduced to the level of the non-hazardous zone.

The use of flammable substances in the industry means that the installations that process them generate explosion hazard zones in the proximal space. Equipment installed and operated in such hazardous areas must not ignite an explosive atmosphere. Confirmation of how equipment is adapted for use in an explosive atmosphere is named explosion protection. Explosion protection is confirmed by testing together with evaluation. For low-current equipment, the situation in which the hazardous atmosphere can be ignited by small components should be considered. The first part of the paper was devoted to the presentation of the explosion hazard and a brief presentation of the classification of equipment intended for use in explosive atmospheres. In the second part, the test methodology for ignition from small components is presented and in the last part, the results of ignition tests from small components are presented. The resulting main conclusion underlines the importance of test conditions.

Computing the amounts of light arriving from different directions enables a diffusely reflecting surface to play the part of a mirror in a periscope—that is, perform non-line-of-sight imaging around an obstruction. Because computational periscopy has so far depended on light-travel distances being proportional to the times of flight, it has mostly been performed with expensive, specialized ultrafast optical systems 1 – 12 . Here we introduce a two-dimensional computational periscopy technique that requires only a single photograph captured with an ordinary digital camera. Our technique recovers the position of an opaque object and the scene behind (but not completely obscured by) the object, when both the object and scene are outside the line of sight of the camera, without requiring controlled or time-varying illumination. Such recovery is based on the visible penumbra of the opaque object having a linear dependence on the hidden scene that can be modelled through ray optics. Non-line-of-sight imaging using inexpensive, ubiquitous equipment may have considerable value in monitoring hazardous environments, navigation and detecting hidden adversaries. A faint penumbra in a photograph of a diffuse surface enables recovery of the position of the object creating the penumbra and an image of the scene behind it.

This paper analyzes the fundamental methods and principles for protecting technical installations used in explosive atmospheres (Ex zones), in order to prevent catastrophic events. The study covers relevant European regulations (ATEX Directive), classification of hazardous areas, and the design and technological solutions that enable safe operation of equipment in such environments. The paper focuses both on theoretical aspects and the practical applicability of explosion protection measures, aiming to increase industrial safety levels.

Cities have historically developed close to rivers and coasts, increasing human exposure to flooding. That exposure is exacerbated by changes in climate and population, and by urban encroachment on floodplains. Although the mechanisms of how urbanization affects flooding are relatively well understood, there have been limited efforts to assess the magnitude of floodplain encroachment globally and how it has changed in both space and time. Highly resolved global datasets of both flood hazard and changes in urban area from 1985 to 2015 are now available, enabling the reconstruction of the history of floodplain encroachment at high spatial resolutions. Here we show that the urbanized area in floodplains that have an average probability of flooding of 1/100 years, has almost doubled since 1985. Further, the rate of urban expansion into these floodplains increased by a factor of 1.5 after the year 2000. We also find that urbanization rates were highest in the most hazardous areas of floodplains, with population growth in these urban floodplains suggesting an accompanying increase in population density. These results reveal the scope, trajectory and extent of global floodplain encroachment. With tangible implications for flood risk management, these data could be directly used with integrated models to assess adaptation pathways for urban flooding.