Cloud and incremental dynamic analysis (IDA) are the two most commonly used methods for seismic fragility analysis. The two methods differ significantly in the number of ground motions and whether these motions are scaled. This paper designed a random selection procedure to thoroughly discuss the influence of ground motion combinations encompassing different numbers of motions on the Cloud-based and IDA-based seismic fragility analysis for underground subway station structures. Focusing on a shallow-buried single-story station structure, a nonlinear dynamic time-history finite element analysis model of soil-structure interaction was developed. 400 ground motions were selected for random combination to perform Cloud-based seismic fragility analysis, and 20 ground motions were selected for random combination to perform IDA-based analysis. The results showed that the number of ground motions has a significant influence on the seismic fragility analysis in both Cloud and IDA, especially on the prediction of damage probability for higher seismic performance levels and when the PGA exceeded 0.3 g. In regions with a low probability of strong earthquakes, this paper recommended using no fewer than 10 and 220 ground motions in the IDA-based and Cloud-based seismic fragility analyses, respectively. In regions with a high probability of strong earthquakes, the optimal number of ground motions should be increased to 300 for Cloud-based analysis and 15 for IDA-based analysis.

Early water stress detection is important for water use yield and sustainability. Traditional methods using the Internet of Things (IoT), such as soil moisture sensors, usually do not provide timely alerts, causing inefficient water use and, in some cases, crop damage. This research presents an innovative early water stress detection method in lettuce plants using Thermal Infrared (TIR) and RGB images in a controlled lab setting. The proposed method integrates advanced image processing techniques, including background elimination via Hue-Saturation- Value (HSV) thresholds, wavelet denoising for thermal image enhancement, RGB-TIR fusion using Principal Component Analysis (PCA), and Gaussian Mixture Model (GMM) clustering to segment stress regions. The leaves stressed areas annotated in the RGB image through yellow pseudo-coloring. This approach is predicated on the fact that when stomata close, transpiration decreases, which causes an increase in the temperature of the affected area. Experimental results reveal that this new approach can detect water stress up to 84 h earlier than conventional soil humidity sensors. Also, a comparative analysis was conducted where key components of the proposed hybrid framework were omitted. The results show inconsistent and inaccurate stress detection when excluding wavelet denoising and PCA fusion. A comparative analysis of image processing performed on a single- board computer (SBC) and through cloud computing over 5 G showed that SBC was 8.27% faster than cloud computing over a 5 G connection. The proposed method offers a more timely and accurate identification of water stress and promises significant benefits in improving crop yield and reducing water usage in indoor farming.

Due to the insufficient burial depth of shallow-buried foundation bridges, foundation voiding easily occurs during floods or rapid water flows. When heavy vehicles pass over these partially voided bridges, the stress state of the foundation deteriorates instantaneously, causing critical components to exceed their load-bearing capacity in a short period, leading to a chain reaction that results in the rapid collapse and overall failure of the bridge structure. Previous numerical simulations of bridge water damage often neglected the strong coupling between water flow, soil, and structure during the scouring process. This paper applies a fluid-solid coupling simulation modeling method for bridge damage behavior under scouring action to study the structural damage behavior of shallow-buried foundation bridges under the combined effects of flood scouring and heavy vehicle load. This method employs point cloud reverse engineering technology to solve the difficult problem of converting the complex scour morphology around the foundation under flood scouring into a structural model, and investigates the multi-hazard damage behavior of shallow-buried foundations by coupling extreme hydraulic effects on the pier surface and placing the most unfavorable heavy vehicle loads on the bridge deck.

The subterranean environment of tunnels poses considerable uncertainty as tunnel structures are ensconced in soil, unlike their above-ground counterparts. This significantly complicates tunnel risk assessment during earthquakes. This study introduces a novel method that integrates multiple damage indices to evaluate the seismic resilience of tunnels. Initially, seismic attenuation is introduced to calculate earthquake exceedance probabilities for various tunnel damage indicators, employing finite element methods (FEM). A robustness evaluation criterion scale value is established based on the amalgamation of multiple tunnel damage indices. Standard Cloud Models are then generated utilising the robustness evaluation criteria. Subsequently, the independent and correlated weights of the robustness evaluation indices are determined using the CRITIC-G1 and decision-making trial and evaluation laboratory (DEMATEL) methods, respectively. A game theory (GT) method is then utilised to amalgamate and allocate weights to these robustness evaluation indices. The evaluation Cloud Models are subsequently generated using a backward cloud generator, based on the division of damage grades for the evaluation criteria and combination weights. Finally, the robustness grade is determined by comparing the similarities between the standard and evaluation Cloud Models. The repair time of the tunnel is quantified using a repair function based on robustness grades. The efficacy of the seismic resilience assessment method is discussed based on three hypothetical cases, providing valuable guidance for assessing the seismic resilience of underground structures.

Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the Extreme Gradient Boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R2 = 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R2 = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design.

Cavities behind the concrete lining of a shield tunnel may result in apparent damage or even collapse of the tunnel during its operation. It is necessary to predict the damage modes of a shield tunnel with cavities, and accordingly reinforce vulnerable areas of the tunnel. This paper investigates the damage modes of shield-tunnel models with cavities at different locations and sizes behind the concrete lining. The tunnel models used in the test are created using a 3D printing technique, with an aim of simulating the joints between segments. To consider the stratum-structure interaction, the tunnel models are created with grout-layers prefabricated between lining and soil. The 3D point cloud technique is then applied to observe the damage modes of the tunnel linings. The safety status of the shield tunnel is evaluated during the loading process, and categorized into safe, dangerous, and failure stages. Experimental results show that the damage modes of the shield tunnel with cavities contain concrete crack, concrete spalling, segment misalignment, and lining crush. Cavities at the tunnel crown and shoulder impose a substantial impact on the lining structure. Cracks propagating across three or more segments result in mutual compression between segments, forming a crack mesh, and consequently leading to concrete spalling. The tunnel lining undergoes a failure mode of segment misalignment when the cavity angle (size) is greater than 45 degrees. As the volume of the cavity increases, the tunnel lining transitions to a failure mode of lining crush. The results in this study will facilitate the proactive reinforcement of the tunnel by predicting damage modes induced by cavities, ensuring its safe operation to a certain extent.

The toxicity of heavy metals to both humans and aquatic life makes them a major environmental concern. Heavy metals such as lead, mercury, cadmium, chromium, and arsenic are major causes of concern. These metals can find their way into water systems by natural processes like soil erosion, as well as industrial ones like mining, electroplating, and metal polishing. They can bioaccumulate in wildlife and offer substantial health concerns to humans through numerous exposure pathways, leading to neurological and developmental disorders, kidney damage and bone degradation, immune system impairments in children, cancer, and skin lesions. Therefore, it is crucial to develop efficient technology for removing heavy metals from contaminated water to safeguard the environment and public health. Promising developments in cloud point extraction (CPE) and photocatalytic nanomaterials could be explored in heavy metal remediation. Photocatalytic nanomaterials can effectively remove heavy metals either by adsorbing or precipitating them. CPE is a very efficient way to separate different types of aqueous solutions to pre-concentrate and remove trace levels of heavy metals from water and wastewater. Although research has demonstrated that CPE and photocatalytic nanomaterials can successfully filter out heavy metals from water, practical applications necessitate the development of more effective and scalable manufacturing processes. Improving extraction conditions, recovering resources, and reusing them are all part of this process, as is creating cost-effective synthesis methods. To make these procedures work with other heavy metal ions, it's important to make them more selective and specific.

Through a comprehensive investigation into the historical profiles of black carbon derived from ice cores, the spatial distributions of light-absorbing impurities in snowpit samples, and carbon isotopic compositions of black carbon in snowpit samples of the Third Pole, we have identified that due to barriers of the Himalayas and remove of wet deposition, local sources rather than those from seriously the polluted South Asia are main contributors of light-absorbing impurities in the inner part of the Third Pole. Therefore, reducing emissions from residents of the Third Pole themselves is a more effective way of protecting the glaciers of the inner Third Pole in terms of reducing concentrations of light-absorbing particles in the atmosphere and on glaciers.

Port pavements often experience damage, such as differential settlements and cracks, owing to soft ground and heavy equipment operations. This study focuses on developing and applying port blocks in two configurations within a port to assess its applicability based on deflection and settlement characteristics. Falling weight deflectometer (FWD) tests were carried out on both asphalt and block pavements to measure deflection and bearing capacity. Results indicate that the block pavement with a cement-treated base exhibited improved bearing capacity and settlement performance during port operations compared to asphalt pavement. This improvement was evident in the relative deflection and relative bearing ratios, where the cement-treated base demonstrated enhanced bearing capacity over asphalt. Light detection and ranging (LiDAR) measurements revealed several settlements in the crushed-stone base due to surface loads post- construction. While both relative deflection and relative bearing ratios indicated settlement tendencies, the latter proved more consistent with the settlements. The settlements were generally less than 5 cm with the superior bearing capacity block pavement presents itself as a viable pavement for various port settings.

Rising temperatures entail important changes in the soil hydrologic processes of the northern permafrost zone. Using the ICON-Earth System Model, we show that a large-scale thaw of essentially impervious frozen soil layers may cause a positive feedback by which permafrost degradation amplifies the causative warming. The thawing of the ground increases its hydraulic connectivity and raises drainage rates which facilitates a drying of the landscapes. This limits evapotranspiration and the formation of low-altitude clouds during the snow-free season. A decrease in summertime cloudiness, in turn, increases the shortwave radiation reaching the surface, hence, temperatures and advances the permafrost degradation. Our simulations further suggest that the consequences of a permafrost cloud feedback may not be limited to the regional scale. For a near-complete loss of the high-latitude permafrost, they show significant temperature impacts on all continents and northern-hemisphere ocean basins that raise the global mean temperature by 0.25 K. Landscapes in the Arctic and subarctic zone are often very wet with highly water saturated soils and an extensive lake- and wetland cover. To some extent, this is due to the perennially frozen soil layers that underlay large parts of these regions and inhibit the movement of water through the ground. Thus, a thawing of the frozen soils, caused by rising temperatures, may ultimately lead to a drying of the landscapes. Here, we use simulations with the ICON-Earth System Model to show that such a drying increases regional temperatures via an atmospheric feedback: During the warm season, dryer conditions at the surface reduce the moisture transport into the atmosphere. This decreases the relative humidity in the boundary layer and the low-altitude cloud cover. Since clouds reflect more sunlight than the snow-free land surface, the reduced cloudiness increases the available energy, hence, temperatures and advances the thawing of the ground. Higher temperatures in the Arctic and subarctic zone, in turn, have important consequences for the net energy exchange between equatorial and polar regions. Thus, the effects of a large-scale drying of high-latitude soils may not be limited to the regional scale but could notably increase global mean temperatures. Advanced degradation of permafrost may facilitate large-scale landscape drying Dependency of clouds on terrestrial hydrology allows for feedback between permafrost thaw, diminished cloudiness and rising temperatures This feedback could amplify global warming notably