Search Results (108)

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations. Full article

Displacement back-analysis is a crucial approach to enhance the effectiveness of landslide monitoring data. To improve the computational efficiency and reliability of large-scale three-dimensional landslide displacement inversion, this study develops a novel Landslide Displacement Intelligent Dynamic Inversion Framework (LDIDIF), which integrates the Bayesian displacement back-analysis (BBA) approach, a Long Short-Term Memory (LSTM) surrogate model, and the RANdom SAmple Consensus (RANSAC) algorithm. Specifically, BBA is employed to dynamically calibrate geotechnical parameters with uncertainty, the LSTM model replaces traditional numerical simulations to reduce computational cost, and RANSAC filters inlier observations to enhance the robustness of the inversion model. A case study of the Dawanzi GNSS landslide is conducted. Results show that the LSTM surrogate model achieves prediction errors below 2 mm and enhances computational efficiency by approximately 50,000 times. The RANSAC algorithm effectively identifies and removes GNSS outliers. Notably, LDIDIF significantly reduces the uncertainty of shear strength parameters within the slip zone, yielding a calibrated displacement precision better than 10 mm. The calibrated model reveals that the landslide is buoyancy-driven and that frontal failure may trigger progressive deformation in the rear slope. These findings offer valuable insights for landslide early warning and reservoir operation planning in the Dawanzi area. Full article

The present study was motivated by the need to enhance indoor air quality and reduce airborne disease transmission in dense urban environments where high-rise residential buildings face challenges in achieving effective natural ventilation. The problem lies in the lack of scalable and convenient tools to optimize natural ventilation rate, particularly in urban settings with varying building heights. To address this, the scientific technique developed with an innovative metric, the urbanized natural ventilation effectiveness factor (uNVeF), integrates regression analysis of wind direction, velocity, air change rate per hour (ACH), window configurations, and building height to quantify ventilation efficiency. By employing a field measurement methodology, the measurements were conducted across 25 window-opening scenarios in a 13.9 m² residential unit on the 35/F of a Hong Kong public housing building, supplemented by the Hellman Exponential Law with a site-specific friction coefficient (0.2907, R² = 0.9232) to estimate the lower floor natural ventilation rate. The results confirm compliance with Hong Kong’s statutory 1.5 ACH requirement (Practice Note for Authorized Persons, Registered Structural Engineers, and Registered Geotechnical Engineers) and achieving a peak ACH at a uNVeF of 0.953 with 75% window opening. The results also revealed that lower floors can maintain 1.5 ACH with adjusted window configurations. Using the Wells–Riley model, the estimation results indicated significant airborne disease infection risk reductions of 96.1% at 35/F and 93.4% at 1/F compared to the 1.5 ACH baseline which demonstrates a strong correlation between ACH, uNVeF and infection risks. The uNVeF framework offers a practical approach to optimize natural ventilation and provides actionable guidelines, together with future research on the scope of validity to refine this technique for residents and developers. The implications in the building industry include setting up sustainable design standards, enhancing public health resilience, supporting policy frameworks for energy-efficient urban planning, and potentially driving innovation in high-rise residential construction and retrofitting globally. Full article

The global shift towards the use of renewable energy is essential to ensure sustainable development, and geothermal energy stands out as a suitable option that can support various cascading projects. Spent geothermal water (SGW) requires proper treatment to ensure that it does not become an environmental burden. Typically, companies often face the dilemma of choosing between discharging spent geothermal water (SGW) into surface waters or injecting it into rock masses, and the economic and environmental impacts of the decision made determines the feasibility of geothermal plant development. In this study, we aimed to comprehensively assess the technical, economic, and environmental feasibility of SGW injection into rock masses. To this end, we employed a comprehensive analytical approach using the Chochołów GT-1 geothermal injection borehole in Poland as a reference case. We also performed drilling and hydrogeological testing, characterized rock samples in the laboratory, and corrected hydrodynamic parameters for thermal lift effects to ensure accurate aquifer characterization. The results obtained highlight the importance of correcting hydrogeological parameters for thermal effects, which if neglected can lead to a significant overestimation of the calculated hydrogeological parameters. Based on our analysis, we developed a framework for assessing SGW injection feasibility that integrates detailed hydrogeological and geotechnical analyses with environmental risk assessment to ensure sustainable geothermal resource exploitation. This framework should be mandatory for planning new geothermal power plants or complexes worldwide. Our results also emphasize the need for adequate SGW management so as to ensure that the benefits of using a renewable and zero-emission resource, such as geothermal energy, are not compromised by the low absorption capacity of rock masses or adverse environmental effects. Full article

Earthwork construction presents significant challenges due to its unique characteristics, including irregular topography, inhomogeneous geotechnical properties, dynamic operations involving heavy equipment, and continuous terrain updates over time. Existing methods often fail to accurately capture these complexities, support semantic attributes, simulate realistic equipment–environment interactions, and update the model dynamically during construction. Moreover, most current digital solutions lack an integrated framework capable of linking geotechnical semantics with construction progress in a continuously evolving terrain. This study introduces a novel, voxel-based digital twin framework tailored for earthwork construction. Unlike previous studies that relied on surface, mesh, or layer-based representations, our approach leverages semantically enriched voxelization to encode spatial, material, and behavioral attributes at a high resolution. The proposed framework connects the physical and digital representations of the earthwork environment and is structured into five modules. The data acquisition module gathers terrain, geotechnical, design, and construction data. Virtual models are created for the earthwork in as-planned and as-built models. The digital twin core module utilizes voxels to create a realistic earthwork environment that integrates the as-planned and as-built models, facilitating model–equipment interaction and updating models for progress monitoring. The visualization and simulation module enables model–equipment interaction based on evolving as-built conditions. Finally, the monitoring and analysis module provides volumetric progress insights, semantic material information, and excavation tracking. The key innovation of this framework lies in multi-resolution voxel modeling, semantic mapping of geotechnical properties, and supporting dynamic updates during ongoing construction, enabling model–equipment interaction and material-specific construction progress monitoring. The framework is validated through real-world case studies, demonstrating its effectiveness in providing realistic representations, model–equipment interactions, and supporting progress information and operational insights. Full article

Ancient temple complexes in China’s mountainous landscapes exemplify a profound synthesis of environmental adaptation and cultural expression. This research investigates the spatial logic underlying the Wudang Mountain temple complex—a UNESCO World Heritage site—through integrated geospatial analysis of environmental factors. Using GIS-based modeling, GeoDetector, and regression analysis, we systematically assess how terrain, hydrology, climate, vegetation, and soil conditions collectively influenced site selection. The results reveal a clear hierarchical clustering pattern, with dense temple cores in the southwestern highlands, ridge-aligned belts, and a dominant southwest–northeast orientation that reflects intentional alignment with mountain ridgelines. Temples consistently occupy zones with moderate thermal, hydrological, and vegetative stability while avoiding geotechnical extremes such as lowland humidity or unstable slopes. Regression analysis confirms that site preferences vary across temple types, with soil pH, porosity, and bulk density emerging as significant influencing factors, particularly for cliffside temples. These findings suggest that ancient temple planning was not merely a passive response to sacred geography but a deliberate process that actively considered terrain, climate, soil, and other environmental factors. While environmental constraints strongly shaped spatial decisions, cultural and symbolic considerations also played an important role. This research deepens our understanding of how environmental factors influenced the formation of historical landscapes and offers theoretical insights and ecologically informed guidance for the conservation of mountain cultural heritage sites. Full article

This study presents a digital twin–based framework for assessing rockfall hazards at the immediate vicinity of the Rumkale Archaeological Site, a geologically sensitive and culturally significant location in southeastern Türkiye. Historically associated with early Christianity and strategically located along the Euphrates, Rumkale is a protected heritage site that attracts increasing numbers of visitors. Here, high-resolution photogrammetric models were generated using imagery acquired from a remotely piloted aircraft system and post-processed with ground control points to produce a spatially accurate 3D digital twin. Field-based geomechanical measurements including discontinuity orientations, joint classifications, and strength parameters were integrated with digital analyses to identify and evaluate hazardous rock blocks. Kinematic assessments conducted in the study revealed susceptibility to planar, wedge, and toppling failures. The results showed the role of lithological structure, active tectonics, and environmental factors in driving slope instability. The proposed methodology demonstrates effective use of digital twin technologies in conjunction with traditional geotechnical techniques, offering a replicable and non-invasive approach for site-scale hazard evaluation and conservation planning in heritage contexts. This work contributes to the advancement of interdisciplinary methods for geohazard-informed management of cultural landscapes. Full article

Unplanned dilution is a critical challenge in underground mining, directly affecting operating costs, resource recovery, stope stability and operational safety. This study presents an empirical–statistical framework that integrates the Mathews–Potvin stability graph, the Equivalent Linear Overbreak/Slough (ELOS) metric, and a site-specific linear calibration to improve dilution prediction in sublevel stoping operations. A database of more than 65 stopes from a Brazilian underground zinc mine was analyzed and classified as cable-bolted, non-cable-bolted, or self-supported. Planned dilution derived from the Potvin graph was compared with actual ELOS measured by cavity-monitoring surveys. Results show a strong correlation between cable-bolted/supported stopes (r = 0.918), whereas non-cabled/unsupported and self-supported stopes display lower correlations (r = 0.755 and 0.767). Applying a site-specific linear calibration lowered the mean absolute dilution error from 0.126 m to 0.101 m (≈20%), with the largest improvement (≈29%) occurring in self-supported stopes where the unadjusted graph is least reliable. Because the equation can be embedded in routine stability calculations, mines can obtain more realistic forecasts without abandoning established empirical workflows. Beyond geotechnical accuracy, the calibrated forecasts improve grade-control decisions, reduce unnecessary waste haulage, and extend resource life—thereby enhancing both the efficiency and the accessibility of mineral resources. This research delivers the first Brazilian case study that couples Potvin analysis with ELOS back-analysis to generate an operational calibration tool, offering a practical pathway for other sites to refine dilution estimates while retaining the simplicity of empirical design. Full article

The reduction in disaster risk in urban regions due to natural hazards (e.g., earthquakes, landslides, floods, and tropical cyclones) is primarily a development matter that must be treated within the scope of a broader urban development framework. Natural hazard assessment is one of the turning points in mitigating disaster risk, which typically contributes to stronger urban resilience and more sustainable urban development. Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horizontal-to-Vertical Spectral Ratio (HVSR) so as to assess liquefaction potential as a natural hazard using AI. The key raw seismic features of site amplification and resonance are extracted from the data via bandpass filtering, Fourier Transformation (FT), the calculation of the HVSR, and smoothing through the use of moving averages. The main novelty is the integration of machine learning, particularly stacked ensemble learning, for liquefaction potential classification from imbalanced seismic datasets. For this approach, several models are used to consider class imbalance, enhancing classification performance and offering better insight into liquefaction risk based on microtremor data. Then, the paper proposes a liquefaction detection method based on deep learning with an autoencoder and stacked classifiers. The autoencoder compresses data into the latent space, underlining the liquefaction features classified by the multi-layer perceptron (MLP) classifier and eXtreme Gradient Boosting (XGB) classifier, and the meta-model combines these outputs to put special emphasis on rare liquefaction events. This proposed methodology improved the detection of an imbalanced dataset, although challenges remain in both interpretability and computational complexity. We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. Key seismic and geotechnical variables were included, confirming the amplification factor (Af) and seismic vulnerability index ($K_g$) as dominant predictors and supporting model generalizability in data-scarce regions. Our proposed method for liquefaction potential classification achieves 100% classification accuracy, 100% precision, and 100% recall, providing a new baseline. Compared to existing models such as XGB and MLP, the proposed model performs better in all metrics. This new approach could become a critical component in assessing liquefaction hazard, contributing to disaster mitigation and urban planning. Full article

Landslides in Ecuador are one of the most common deadly events in natural hazards, such as the one on 26 March 2023. A large-scale landslide occurred in Alausí, Chimborazo province, causing 65 fatalities and 10 people to disappear, significant infrastructural damage, and the destruction of six neighborhoods. This study presents a detailed case analysis of the anthropogenic factors that could have contributed to the instability of the affected area. Field investigations and a review of historical, geological, and social information are the basis for analyzing the complex interactions between natural and human-induced conditions. Key anthropogenic contributors identified include unplanned urban expansion, ineffective drainage systems, deforestation, road construction without adequate geotechnical support, and changes in land use, particularly agricultural irrigation and wastewater disposal. These factors increased the area’s susceptibility to slope failure, which, combined with intense rainfall and past seismic activity, could have caused the rupture process’s acceleration. The study also emphasizes integrating geological, hydrological, and urban planning assessments to mitigate landslide risks in geologically sensitive regions such as Alausí canton. The findings conclude that human activity could be an acceleration factor in natural processes, and the pressure of urbanization amplifies the consequences. This research underscores the importance of sustainable land management, improved drainage infrastructure, and land-use planning in hazard-prone areas. The lessons learned from Alausí can inform risk reduction strategies across other mountainous and densely populated regions worldwide, like the Andean countries, which have similar social and environmental conditions to Ecuador. Full article

Subsurface characterisation is a fundamental aspect of the planning and design of hydroelectric projects, as it enables the assessment of the technical and geotechnical feasibility of the proposed infrastructure, ensuring its stability and functionality. This study focuses on the characterisation of rock masses from boreholes in the “Santa Rosa” and “El Rosario” areas, located in Morona Santiago, Ecuador, to determine key parameters for the design of hydroelectric projects. Field and laboratory tests were conducted, including uniaxial compression tests, indirect tensile–Brazilian tests, point load tests, tilt tests, and geomechanical classifications using the RMR and Q systems. The results show that igneous rocks, such as basalt and andesite, exhibit mechanical properties ranging from moderate to high, with uniaxial compressive strengths exceeding 120 MPa in the case of basalt, classifying it as a strong rock. In contrast, metamorphic rocks, such as chert, exhibit lower strength, with values ranging between 69.69 MPa and 90.63 MPa, classifying them as moderately strong. The RMR and Q index values indicate a variable rock mass quality, ranging from excellent in diorite and granite sectors to low in areas with significant discontinuities and alterations. Additionally, variations in basic friction angles were identified, ranging from 18° to 38°, which directly influence the stability of the proposed structures. In conclusion, this study highlights the importance of geomechanical characterisation in ensuring the technical feasibility of hydroelectric projects, providing key information for the design and development of safe and sustainable infrastructure in the region. Full article

The coastal region of Ceará, Brazil, is expected to host offshore wind farms aimed at producing green hydrogen (GH₂) through electrolysis. However, the viability and cost of these developments may be affected by the mechanical behaviour of the marine subsoil, which is largely composed of carbonate sands. These sediments are known for their complex and variable geotechnical properties, which can influence the foundation performance. This study investigates the geotechnical characteristics of carbonate sands in comparison with quartz sands to support the design of offshore wind turbine foundations. Field testing using the Ménard pressuremeter and laboratory analyses, including particle size distribution, microscopy, X-ray fluorescence, calcimetry, direct shear, and triaxial testing, were performed to determine the key strength and stiffness parameters. The results show substantial differences between carbonate and quartz sands, particularly in terms of the stiffness and friction angle, with notable variability even within the same material type. These findings highlight the need for site-specific characterisation in offshore foundation design. This study contributes data that can improve geotechnical risk assessments and assist in selecting appropriate foundation solutions under local conditions, supporting the planned offshore wind energy infrastructure essential to Ceará’s green hydrogen strategy. Full article

This study assessed riverbank erosion and stability along the Mekong and Bassac Rivers to propose safe river corridors and mitigate erosion risks in the Mekong Delta. Using Landsat imagery (2000–2023), field surveys, and numerical simulations, we identified severe erosion hotspots, where erosion rates reach up to 40 m annually, in the meandering sections of the Mekong River,. In contrast, the Bassac River exhibited significant sedimentation, though this trend was diminishing due to upstream sediment deficits caused by hydropower dams. Stability assessments revealed optimal safety corridor distances ranging from 20 to 38 m, influenced by local geotechnical conditions and structural loads. A significant proportion of riverbanks in Dong Thap (88%) and An Giang (48%) do not comply with conservation standards, exacerbating erosion risks and threatening infrastructure. The results of this study highlight the urgent need for enforcing conservation regulations, implementing nature-based solutions like riparian buffers, and adopting sustainable land-use planning. By addressing the interplay between natural processes and anthropogenic pressures, these findings offer actionable insights to enhance riverbank stability, protect ecosystems, and sustain livelihoods in the Mekong Delta amidst growing environmental challenges. Full article

The construction industry is undergoing a transformative shift through automation, with advancements in Generative AI (GenAI) and prompt engineering enhancing safety and efficiency, particularly in high-risk fields like underground construction, geotechnics, and mining. In underground construction, GenAI-powered prompts are revolutionizing practices by enabling a shift from reactive to predictive approaches, leading to advancements in design, project planning, and site management. This study explores the use of Google Gemini, a recent advancement in GenAI, for the prediction of rockburst intensity levels in underground construction. The Python programming language and the Google Gemini tool are combined with prompt engineering to generate prompts that incorporate essential variables related to rockburst. A comprehensive database of 93 documented rockburst cases is compiled. Subsequently, a systematic method is established that involves the categorization of intensity levels through data visualization and factor analysis in order to identify a reduced number of unobservable underlying factors. Furthermore, K-means clustering is utilized to identify data patterns. The gradient boosting classifier is then employed to predict the intensity levels of rockburst. The results demonstrate that GenAI and prompt engineering offers an effective approach for accurately predicting rockburst events, achieving an accuracy rate of 89 percent. Through predictive modeling with GenAI, construction engineering experts can proactively evaluate the likelihood of rockburst, allowing for improved risk management, optimized excavation strategies, and enhanced safety protocols. This approach enables the automation of complex analyses and provides a powerful tool for real-time decision-making and predictive insights, offering significant benefits to industries reliant on underground construction. However, despite the considerable potential of GenAI and prompt engineering in the construction sector, challenges related to output accuracy, the dynamic nature of projects, and the need for human oversight must be carefully addressed to ensure effective implementation. Full article

In this research, the mud diapirism phenomenon in the Membrillal sector in Cartagena is characterized to analyze its spatiotemporal evolution. The goal is to geomorphologically, geotechnically, and geologically characterize the area to zone regions with the greatest susceptibility to geological hazards and provide an updated diagnosis of the phenomenon. This study is conducted due to the risks that mud diapirism poses to infrastructure and the safety of local communities. Understanding the behavior of these structures is essential for designing effective mitigation measures and optimizing urban planning in areas affected by this phenomenon. The methodology used includes collecting secondary data and implementing geophysical, geotechnical, and laboratory tests. Among the techniques employed are the Standard Penetration Test (SPT), the excavation of test pits, and electrical resistivity tomography, which revealed mud deposits at different depths. Laboratory studies also evaluated the physical and mechanical properties of the soil, such as Atterberg limits, grain size distribution, moisture content, and expansion tests, in addition to physic-chemical analyses. Among the most relevant findings is the presence of four active mud vents and four mud ears, representing an increase compared to the previous study that only recorded three mud vents. The tests revealed mud deposits at 1.30 m and 10 m depths, consistent with the geotechnical results. Laboratory tests revealed highly plastic soils, with Liquid Limits (LL) ranging from 44% to 93% and Plastic Limits (PL) ranging from 14% to 46%. Soil classification showed various low- and high-plasticity clays (CL and CH) and silty clays (MH), presenting challenges for structural stability and foundation design. Additionally, natural moisture content varied between 15.8% and 89%, and specific gravity ranged from 1.72 to 2.75, reflecting significant differences in water retention and soil density. It is concluded that diapirism has increased in the region, with constant monitoring recommended, and the Territorial Planning Plan (POT) has been updated to include regulations that mitigate the risks associated with urban development in affected areas. Full article