Search Results (1,074)

During offshore operations of jack-up platforms, the spudcan may experience sudden punch-through failure when penetrating from an overlying stiff clay layer into the underlying soft clay, posing significant risks to platform safety. Conventional punch-through prediction methods, which rely on predetermined soil parameters, exhibit limited accuracy as they fail to account for uncertainties in seabed stratigraphy and soil properties. To address this limitation, based on a database of centrifuge model tests, a probabilistic prediction framework for the peak resistance and corresponding depth is developed by integrating empirical prediction formulas based on Bayes’ theorem. The proposed Bayesian methodology effectively refines prediction accuracy by quantifying uncertainties in soil parameters, spudcan geometry, and computational models. Specifically, it establishes prior probability distributions of peak resistance and depth through Monte Carlo simulations, then updates these distributions in real time using field monitoring data during spudcan penetration. The results demonstrate that both the recommended method specified in ISO 19905-1 and an existing deterministic model tend to yield conservative estimates. This approach can significantly improve the predicted accuracy of the peak resistance compared with deterministic methods. Additionally, it shows that the most probable failure zone converges toward the actual punch-through point as more monitoring data is incorporated. The enhanced prediction capability provides critical decision support for mitigating punch-through potential during offshore jack-up operations, thereby advancing the safety and reliability of marine engineering practices. Full article

Screwed connections are widely adopted in timber–concrete composite (TCC) structures. Owing to the diverse connection configurations and complex shear mechanisms, existing empirical models or theoretical formulas cannot accurately and efficiently predict the shear modulus of a screwed connection. Therefore, this study develops machine learning (ML) algorithms to accurately predict the slip modulus. A data set including 222 sets of testing results was established by collecting the values of the slip modulus and associated ten features. Four ML methods, including decision tree (DT), random forest (RF), adaptive boosting machine (AdaBoost), and gradient boosting regression tree (GBRT), are adopted to develop the ML algorithm. The Shapley Additive Explanation (SHAP) framework was employed to interpret the effects of related features on the slip modulus. GBRT demonstrated the best accuracy compared with the other three ML methods in terms of four popular quantitative metrics. Moreover, all ML methods showed an evident accuracy advantage compared to existing analytical methods. Through a SHAP analysis, it was found that concrete strength, screw inclination, timber density, and timber type have a large impact on the slip modulus of a screwed connection compared to other input features. Full article

The influence of foundation pit excavation on the bearing behavior of concrete tubular piles at the pit bottom remains unclear. Based on the Vesic cavity expansion theory, this paper proposes a method for calculating pile driving resistance, which takes into account the residual effect of vertical pressure changes on earth pressure during excavation. Furthermore, relying on the statistical regularity between Qu/Pu (ratio of ultimate bearing capacity to ultimate cavity expansion pressure) and L/d (length-to-diameter ratio), theoretical formulas for calculating the ultimate bearing capacity of tubular piles before and after foundation pit excavation are established, with their reliability and influencing factors analyzed. This method only requires determining the L/d of the tubular piles and the theoretical value of pile driving resistance. With its simple parameter requirements, it is suitable for estimating the ultimate bearing capacity of tubular piles affected by excavation. By comparing the computed penetration resistance, earth pressure, and driving resistance of tubular piles with field measurements, the computed results show good agreement with field measurements, and the accuracy of the proposed method meets the requirements of engineering design, verifying its feasibility as an empirical method. The fitting results of the Q_u/P_u ratios indicate that the deviations between the measured and computed values are 4.17% and 5.64% before and after excavation, respectively. Additionally, L/d and L/H (ratio of pile length to excavation depth) significantly affect the earth pressure, driving resistance, and vertical bearing capacity of monopoles. Smaller L/d and L/H ratios lead to greater earth pressure on the pile and more pronounced effects on driving resistance and vertical bearing capacity. The development of this method offers an approach for estimating the ultimate bearing capacity of tubular piles before and after foundation pit excavation during preliminary design, thereby holding substantial engineering significance. Full article

The new mineral achyrophanite (K,Na)₃(Fe³⁺,Ti,Al,Mg)₅O₂(AsO₄)₅ was found in high-temperature sublimates of the Arsenatnaya fumarole at the Second scoria cone of the Northern Breakthrough of the Great Tolbachik Fissure Eruption, Tolbachik volcano, Kamchatka, Russia. It is associated with aphthitalite-group sulfates, hematite, alluaudite-group arsenates (badalovite, calciojohillerite, johillerite, nickenichite, hatertite, and khrenovite), ozerovaite, pansnerite, arsenatrotitanite, yurmarinite, svabite, tilasite, katiarsite, yurgensonite, As-bearing sanidine, anhydrite, rutile, cassiterite, and pseudobrookite. Achyrophanite occurs as long-prismatic to acicular or, rarer, tabular crystals up to 0.02 × 0.2 × 1.5 mm, which form parallel, radiating, bush-like, or chaotic aggregates up to 3 mm across. It is transparent, straw-yellow to golden yellow, with strong vitreous luster. The mineral is brittle, with (001) perfect cleavage. D_calc is 3.814 g cm^–3. Achyrophanite is optically biaxial (+), α = 1.823(7), β = 1.840(7), γ = 1.895(7) (589 nm), 2V (meas.) = 60(10)°. Chemical composition (wt.%, electron microprobe) is: Na₂O 3.68, K₂O 9.32, CaO 0.38, MgO 1.37, MnO 0.08, CuO 0.82, ZnO 0.48, Al₂O₃ 2.09, Fe₂O₃ 20.42, SiO₂ 0.12, TiO₂ 7.35, P₂O₅ 0.14, V₂O₅ 0.33, As₂O₅ 51.88, SO₃ 1.04, and total 99.40. The empirical formula calculated based on 22 O apfu is Na_1.29K_2.15Ca_0.07Mg_0.34Mn_0.01Cu_0.11Zn_0.06Al_0.44Fe³⁺_2.77Ti_1.00Si_0.02P_0.02S_0.14V_0.04As_4.90O₂₂. Achyrophanite is orthorhombic, space group P222₁, a = 6.5824(2), b = 13.2488(4), c = 10.7613(3) Å, V = 938.48(5) ų and Z = 2. The strongest reflections of the PXRD pattern [d,Å(I)(hkl)] are 5.615(59)(101), 4.174(42)(022), 3.669(31)(130), 3.148(33)(103), 2.852(43)(141), 2.814(100)(042, 202), 2.689(29)(004), and 2.237(28)(152). The crystal structure of achyrophanite (solved from single-crystal XRD data, R = 4.47%) is unique. It is based on the octahedral-tetrahedral M-T-O pseudo-framework (M = Fe³⁺ with admixed Ti, Al, Mg, Na; T = As⁵⁺). Large-cation A sites (A = K, Na) are located in the channels of the pseudo-framework. The achyrophanite structure can be described as stuffed, with the defect heteropolyhedral pseudo-framework derivative of the orthorhombic Fe³⁺AsO₄ archetype. The mineral is named from the Greek άχυρον, straw, and φαίνομαι, to appear, in allusion to its typical straw-yellow color and long prismatic habit of crystals. Full article

This paper develops an analytical turbulence model for open-ended squeeze film damper (SFD) application. Prandtl’s mixing length theory is adopted to describe the momentum transfer within the damper for its thin-film turbulent flow. A novel turbulence coefficient function is developed to describe the effective fluid viscosity such that the classical Reynolds equation remains applicable. Model validation is presented by (i) comparing the damping coefficient obtained by several existing empirical formulas and (ii) correlating the rotor dynamic prediction with the experimental measurement of an integrated rotor-SFD test rig. This work provides a reduced form of turbulence coefficient for certain SFD implementations. It quantifies the turbulence effect under different operating conditions, which is valued as a practical tool to assess the significance of turbulence consequences in rotor dynamic applications. Full article

In this study, we show how uniform hazard spectra (UHS) can contribute to sustainable development in regions with frequent moderate to strong seismic events and a variety of deeper geological conditions, by reducing seismic risks and enhancing resilience. The case study region surrounds a site at Banja Luka, Bosnia and Herzegovina. Frequency-dependent scaling equations are presented. UHS spectra for Banja Luka are calculated utilizing regional seismicity estimations, deep geology data, and the regional empirical formulae for scaling different PSA amplitudes. The UHS amplitudes are compared with Eurocode 8 spectra. The results demonstrate that the ratios of the highest UHS amplitudes to the corresponding PGA values differ significantly from 2.5, which is the factor specified by Eurocode 8 for the horizontal ground motion. The results also suggest that the influence of deep geology on UHS amplitudes can outweigh local soil effects. For example, at the vibration period of 0.1 s, the largest site effects are obtained for deep geology when comparing the UHS amplitude at geological rock to that at intermediate sites. In this case, the deep geology amplification of 1.47 is 19% higher than the local soil amplification of 1.24 for the same vibration period at the stiff soil sites compared to the rock soil sites. The UHS obtained may be interpreted as preliminary for Banja Luka and other places with similar deep geology, local soil conditions, and seismicity. When the quantity of strong-motion data in the region increases significantly beyond what it is now, it will be possible to correctly calibrate the existing attenuation equations and obtain more reliable UHS estimates. Full article

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

Fracture energy plays a pivotal role in ensuring the safe design of concrete structures. Currently, experimental testing remains the predominant methodology for exploring fracture energy in concrete. Nevertheless, this approach is hindered by protracted sample production cycles and test loading conditions that contribute to elevated expenses. Moreover, owing to the complex nonlinear behavior exhibited by concrete during the fracturing process, existing empirical formulas exhibit restricted precision when forecasting fracture energy. Therefore, in order to swiftly and accurately predict the fracture energy of concrete and investigate the impact of various factors on it, this study employs a deep learning algorithm to establish the correlation between parameters and fracture energy. Additionally, an interpretable deep learning prediction model for fracture energy is proposed, which is then compared with existing empirical formulas. Finally, the SHapley Additive exPlanations (SHAP) interpretability method is utilized to interpret and analyze the prediction results. The SHAP method can identify and visualize the contribution direction (positive/negative) and magnitude of the input features and reveal the relative importance of parameters at both local and global levels simultaneously. This analysis effectively explains the decision-making mechanism of the “black box” model and significantly improves the problem of insufficient interpretability that is common in traditional machine learning methods. The findings demonstrate that over 87% of the prediction results from the deep learning model in this study exhibit a relative error of less than 10% on the test set. The model effectively captures the intricate nonlinear relationship among characteristic parameters, exhibiting superior accuracy and generalization capabilities compared to empirical formulas. The SHAP values of the input parameters are visualized to assess their influence on fracture energy: initially, fracture energy increases and then decreases with increasing compressive strength, age, and coarse aggregate proportion; fracture energy increases with increasing maximum particle size of aggregate until it reaches 20 mm, after which it stabilizes; a high water–binder ratio reduces fracture energy; within the range of 400 mm, fracture energy increases with height, exhibiting a noticeable size effect; fracture energy increases with specimen width, but the size effect diminishes beyond 150 mm width; fracture energy decreases as span–height ratio increases; seam height ratio exhibits an initial increase followed by a decrease in fracture energy, with larger ratios showing a more pronounced size effect; an increase in ligament height enhances fracture energy while maintaining a significant size effect. Full article

Local scour is a phenomenon of sediment erosion and transport caused by the dynamic interaction between water flow and seabed sediment, posing a serious threat to the safety of marine engineering structures such as cross-sea bridges and offshore wind turbines. To improve scour prediction and prevention capabilities, this review systematically analyzes the influence mechanisms of factors such as hydrodynamic conditions, sediment characteristics, and structural geometry, and discusses scour protection measures. Based on this, a comprehensive evaluation of the applicability of different prediction methods, including traditional empirical formulas, numerical simulations, probabilistic prediction models, and machine learning (ML) methods, was conducted. The study focuses on analyzing the limitations of existing methods: empirical formulas lack adaptability under complex field conditions, numerical simulation still faces challenges in validating real marine environments, and data-driven models suffer from “black box” issues and insufficient generalization capabilities. Based on the current research progress, this review presents prospects for future development, emphasizing the need to deepen the study of scouring mechanisms in complex real marine environments, develop efficient numerical models for engineering applications, and explore intelligent prediction methods that integrate data-driven approaches with physical mechanisms. This aims to provide more reliable theoretical support for the safe design, risk prevention, and scouring mitigation measures in marine engineering. Full article

As the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among mines, accurately calculating pipeline resistance loss remains challenging, resulting in significant inaccuracies. The rapid development of Industry 4.0 provides intelligent and data-driven optimization ideas for this challenge. This study introduces a novel pipeline resistance loss prediction framework integrating generative artificial intelligence with a TransKAN model. This study employs generative artificial intelligence to produce physically constrained augmented data, integrates the KAN network’s B-spline basis functions for nonlinear feature extraction, and incorporates the Transformer architecture to capture spatio-temporal correlations in pipeline pressure sequences, enabling precise resistance loss calculation. The experimental data collected from pipeline pressure sensors provides empirical validation for the model. Compared with traditional mathematical formulas, BP neural networks, SVMs, and random forests, the proposed model demonstrates superior performance, achieving an R² value of 0.9644, an RMSE of 0.7126, and an MAE of 0.4703. Full article

In high-elevation or high-latitude permafrost areas, persistent subzero temperatures significantly impact the freeze–thaw durability of concrete structures. Traditional methods for studying the frost resistance of concrete in permafrost regions do not provide a complete picture for predicting properties, and new approaches are needed using, for example, machine learning algorithms. This study utilizes four machine learning models—Support Vector Machine (SVM), extreme learning machine (ELM), long short-term memory (LSTM), and radial basis function neural network (RBFNN)—to predict freeze–thaw damage factors in concrete under low and subzero temperature conservation conditions. Building on the prediction results, the optimal model is refined to develop a new machine learning model: the Sparrow Search Algorithm-optimized Extreme Learning Machine (SSA-ELM). Furthermore, the SHapley Additive exPlanations (SHAP) value analysis method is employed to interpret this model, clarifying the relationship between factors affecting the freezing resistance of concrete and freeze–thaw damage factors. In conclusion, the empirical formula for concrete freeze–thaw damage is compared and validated against the prediction results from the SSA-ELM model. The study results indicate that the SSA-ELM model offers the most accurate predictions for concrete freeze–thaw resistance compared to the SVM, ELM, LSTM, and RBFNN models. SHAP value analysis quantitatively confirms that the number of freeze–thaw cycles is the most significant input parameter affecting the freeze–thaw damage coefficient of concrete. Comparative analysis shows that the accuracy of the SSA-ELMDE prediction set is improved by 15.46%, 9.19%, 21.79%, and 11.76%, respectively, compared with the prediction results of SVM, ELM, LSTM, and RBF. This parameter positively influences the prediction results for the freeze–thaw damage coefficient. Curing humidity has the least influence on the freeze–thaw damage factor of concrete. Comparing the prediction results with empirical formulas shows that the machine learning model provides more accurate predictions. This introduces a new approach for predicting the extent of freeze–thaw damage to concrete under low and subzero temperature conservation conditions. Full article

This study addresses the challenge of predicting the airtightness of stratospheric airship envelopes, a critical factor influencing flight performance. Traditional ground-based airtightness tests often rely on limited resources and empirical formulas. To overcome these limitations, this paper explores the use of predictive models to integrate multi-source test data, enhancing the accuracy of airtightness assessments. A performance comparison of various prediction models was conducted using ground-based test data from a specific stratospheric airship. Among the models evaluated, the NeuralProphet model demonstrated superior accuracy in long-term airtightness predictions, effectively capturing time-series dependencies and spatial interactions with environmental conditions. This work introduces an innovative approach to modeling airtightness, providing both experimental and theoretical contributions to the field of stratospheric airship performance prediction. Full article

This study aims to describe the triphylite (LiFe²⁺PO₄) from Li-bearing pegmatites from the Conțu-Negovanu area (Southern Carpathians, Romania). Thus, for the first time in this area, using four analytical methods, i.e., electron micro-probe analysis (EMPA), polarized optical microscopy (POM), Fourier transform infrared spectroscopy (FTIR), and powder X-ray diffraction (p-XRD), the authors have succeeded in isolating the triphylite from the isomorphous triphylite–lithiophilite series. In addition, in the Conțu-Negovanu area, two new minerals were identified and described for the first time in pegmatites from this area: Fe-rich gatehouseite and wolfeite. The use of EMPA allowed for the tentative calculation of empirical formulae for these secondary phosphate minerals. Full article

To address the design and optimization of the ignition system for the microgravity single-droplet combustion experiment module within the Combustion Science Experimental System (CSES) aboard the Chinese Space Station (CSS), it is essential to first determine the ignition temperatures required for typical liquid fuel droplets. In this study, ignition experiments were conducted on droplets of three representative hydrocarbon fuels—ethanol, n-heptane, and n-dodecane—in static air at high temperatures ranging from 760 K to 1100 K. The experimental results show that the initial droplet diameter is inversely correlated with the ambient temperature at which ignition occurs. Subsequently, based on Frank-Kamenetskii’s analytical method and combined with experimental data, a semi-empirical predictive model for droplet ignition temperatures in a normal-gravity environment was derived. Building upon this, and considering the characteristics of the microgravity environment, an appropriate empirical formula was applied to refine the model, resulting in a predictive model for droplet ignition temperatures in the microgravity environment. Furthermore, by comparing the experimental data and the observed phenomena from existing microgravity experiments, this semi-empirical predictive model used in the microgravity environment effectively reflects the trend of droplet ignition temperature variations. Full article

Landslide-induced impulse waves (LIIWs) are significant natural hazards, frequently occurring in mountain reservoirs, which threaten the safety of waterways and dam project. To predict the impact of impulse waves induced by Rongsong (RS) potential landslide on the dam, during the layered construction period and maximum water level operation period of Rumei (RM) Dam (unbuilt), a large-scale three-dimensional similar physical model with a similarity scale of 200:1 (prototype length to model length) was established. The experiments set five water levels during the dam’s layered construction period and recorded and analyzed the generation and propagation laws of LIIWs. The findings indicate that, for partially granular submerged landslides, no splashing waves are generated, and the waveform of the first wave remains intact. The amplitude of the first wave exhibits stable attenuation while the third one reaches the largest. After the first three columns of impulse waves, water on the dam surface oscillates between the two banks. This study specifically discusses the impact of different water depths on LIIWs. The results show that the wave height increases as the water depth decreases. Two empirical formulas to calculate the wave attenuation at the generation area and to calculate the maximum vertical run-up height on the dam surface were derived, showing strong agreement between the empirical formulas and experimental values. Based on the model experiment results, the wave height data in front of the RM dam during the construction and operation periods of the RM reservoir were predicted, and engineering suggestions were given for the safety height of the cofferdam during the construction and security measures to prevent LIIW overflow the dam top during the operation periods of the RM dam. Full article