Prediction Of Capacity Research Articles

Temperature-dependent debonding behavior of adhesively bonded CFRP-UHPC interface

The adhesively bonded structure, comprising carbon fiber-reinforced polymer (CFRP) and ultra-high-performance concrete (UHPC), enhances structural strength while reducing brittleness and strain softening behavior. However, the adhesively bonded structure is inevitably influenced by temperature variations throughout its service life. When the adhesive layer's temperature surpasses the glass transition temperature (Tg), significant damage occurs to the bond layer joint. To investigate the debonding behavior of the CFRP-UHPC interface at various temperatures, a series of three-point-bending tests were conducted at room temperature, 120 %Tg, 150 %Tg, and 160 %Tg. This study introduced a temperature degradation factor, employing a proposed maximum load capacity prediction formula to adjust both the test results and data from other researchers. By adjusting the cohesion parameters with the temperature degradation factor, a temperature-dependent mixed-mode cohesive zone model (CZM) was developed. The model's validity was confirmed through finite element (FE) analysis, considering two distinct experimental scenarios. The paper concludes that the proposed model effectively simulates interface debonding behavior across varying temperatures.

Machine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiber

Machine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiber

Study on ultimate capacity and design method of cold-formed steel equal lipped angle components under axial compression

This study focuses on evaluating the load-bearing capacity of cold-formed steel (CFS) equal lipped angle components. It delves into the structural behavior and design methods, taking into account various edge conditions and buckling modes. By considering the key parameters, which are component length, component thickness, total lip length, and lip bending angle, a total of 555 numerical models are developed for a systematic analysis on the components with two types of cross-sectional shapes. Since the coefficients within the direct strength method (DSM) inadequately capture the ultimate load-bearing capacity of lipped angle components, we introduce a design methodology rooted in the DSM framework to address global and global-local coupling buckling modes while imposing constraints to prevent local-distortion coupling buckling modes. The machine learning models (XGBoost, BPNN, and 1D-CNN) are adopted to predict the structural behavior of the two types of lipped angle components. These models can integrate the analysis of the components with two types of cross-section shapes in predictions of ultimate load capacity, axial deformation, and buckling modes. Meanwhile, the prediction accuracy of the machine learning models surpasses that of the analytical design methodology, offering more accurate prediction for ultimate load capacity, axial deformation and buckling modes.

Modelling soil-water retention curves subject to multiple wetting-drying cycles: An approach for expansive soils

This study proposes an innovative and simple method to quantify the effect of wetting–drying history on soil–water retention curve (SWRC) of expansive soils. This method is based on the increment relationship between degree of saturation and initial void ratio corresponding to irreversible swelling or shrinkage after each wetting–drying cycle, following the double-structure scheme for three-phase reactive porous media. The approach satisfies the intrinsic constraints for partially saturated porous media, and the incremental relationship can be applied in any existing SWRC equations for future water retention capacity prediction. In this respect, only one new rate function is proposed, which could be easily calibrated by relevant experimental data. The prediction of the model is verified through the comparison with relevant experimental data of two expansive clayey soils. To evaluate the general applicability of the proposed method, two typical SWRC equations proposed in literature were used. The results showed a very good agreement with experimental data subject to multiple wetting–drying cycles, indicating its potential as an effective tool for estimating preliminary SWRCs of expansive soils.

Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm

Due to the large-scale application of electric vehicles, the remaining service life prediction and health status diagnosis of lithium-ion batteries as their power core is particularly important, and the essence of RUL prediction and SOH diagnosis is the prediction of remaining capacity. Through the aging experiment of cycle charging and discharging of lithium-ion batteries, the health features of experimental data are extracted for the prediction of remaining capacity. In this paper, a deep feature extraction method based on Bilinear CNN combined with CatBoost algorithm based on fractional order method optimization particle swarm optimization, and ant colony optimization algorithm is proposed for battery remaining capacity prediction. Seven groups of health features extracted from ten groups of battery data were used to input them into the optimized CatBoost algorithm for regression prediction. The results show that the proposed model achieves accurate SOH and RUL prediction, the three evaluation indicators MAE, RMSE, and MAPE of SOH are all within 1.7 % and the error rate of RUL is not higher than 1.5 %, and the test of multiple batteries also proves its strong robustness.

Bearing capacity of shallow foundations: a focus on the depth factors in combination with the respective N-factors

The ongoing refinement of bearing capacity equations remains pivotal in soil mechanics and foundation engineering, reflecting its critical role in ensuring design efficacy and construction safety. This study conducts a thorough evaluation of classical bearing capacity methods—Terzaghi, Meyerhof, Vesic, and Hansen—and methods included in various design standards, such as EN1997:2004, prEN1997:2023, GEO, AASHTO, FHWA, and API. It explores the performance and applicability of these approaches, identifying areas for potential improvement. In response to identified challenges, the paper proposes the integration of a unified depth factor. This new factor is designed to be applicable across all N-terms, providing a more versatile and accurate tool for bearing capacity predictions. Unlike the original depth factors unique to each method, which may not fully address complex soil and footing conditions, the unified depth factor is developed to enhance prediction accuracy for a wide range of conditions, including both flexible and rigid footings under varying flow rules (ψ = 0 and ψ = φ). This depth factor corrects for modeling errors, emphasizing the importance of pairing the correct set of N-factors with their corresponding depth factor. By offering a singular depth factor that aligns with the outcomes of finite element analysis, this paper not only simplifies the computational process but also enhances the accuracy of bearing capacity predictions across a diverse range of soil conditions and footing types. The comparative analysis, based on finite element analysis, validates the proposed method’s effectiveness, showcasing its potential to significantly refine foundation design practices by comparing it with both traditional and newly developed depth factors.

Crystal Phase Ionic Liquids for Energy Applications: Heat Capacity Prediction via a Hybrid Group Contribution Approach.

In the selection and design of ionic liquids (ILs) for various applications, including heat transfer fluids, thermal energy storage materials, fuel cells, and solvents for chemical processes, heat capacity is a key thermodynamic property. While several attempts have been made to develop predictive models for the estimation of the heat capacity of ILs in their liquid phase, none so far have been reported for the ILs' solid crystal phase. This is particularly important for applications where ILs will be used for thermal energy storage in the solid phase. For the first time, a model has been developed and used for the prediction of crystal phase heat capacity based on extending and modifying a previously developed hybrid group contribution model (GCM) for liquid phase heat capacity. A comprehensive database of over 5000 data points with 71 unique crystal phase ILs, comprising 42 different cations and 23 different anions, was used for parameterization and testing. This hybrid model takes into account the effect of the anion core, cation core, and subgroups within cations and anions, in addition to the derived indirect parameters that reflect the effects of branching and distribution around the core of the IL. According to the results, the developed GCM can reliably predict the crystal phase heat capacity with a mean absolute percentage error of 6.78%. This study aims to fill this current gap in the literature and to enable the design of ILs for thermal energy storage and other solid phase applications.

Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls

AbstractIn this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block‐based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake‐like loads and differential settlements). For a damaged masonry wall, we extract the crack width cumulative distribution, we derive a crack width exceedance curve (CWEC), and we evaluate the drift loss (DL) with respect to the undamaged wall. Numerous pairs of CWEC and DL are thus generated and used for training (and validating) an ensemble of CNNs generated via repeated ‐folding cross validation with shuffling. As a result, a method for damage prognosis (Level IV of SHM) is provided. Such method appears general, inexpensive, and able to adequately predict the DL using as only input the CWEC, providing real‐time support for decision making in damaged masonry structures.

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3–N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3–N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 × 107 CNY, and the treatment cost was 2.04 × 108 CNY to make NH3–N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19,488.51 t/a and 8.24 × 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

Developing a novel big dataset and a deep neural network to predict the bearing capacity of a ring footing

The accurate prediction of the bearing capacity of ring footings, which is crucial for civil engineering projects, has historically posed significant challenges. Previous research in this area has been constrained by considering only a limited number of parameters or utilizing relatively small datasets. To overcome these limitations, a comprehensive finite element limit analysis (FELA) was conducted to predict the bearing capacity of ring footings. The study considered a range of effective parameters, including clay undrained shear strength, heterogeneity factor of clay, soil friction angle of the sand layer, radius ratio of the ring footing, sand layer thickness, and the interface between the ring footing and the soil. An extensive dataset comprising 80,000 samples was assembled, exceeding the limitations of previous research. The availability of this dataset enabled more robust and statistically significant analyses and predictions of ring footing bearing capacity. In light of the time-intensive nature of gathering a substantial dataset, a customized deep neural network (DNN) was developed specifically to predict the bearing capacity of the dataset rapidly. Both computational and comparative results indicate that the proposed DNN (i.e. DNN-4) can accurately predict the bearing capacity of a soil with an R2 value greater than 0.99 and a mean squared error (MSE) below 0.009 in a fraction of 1 s, reflecting the effectiveness and efficiency of the proposed method.

Research on shear characteristics of pile–soil interface under cyclic loading

Offshore wind turbine constructions are developing rapidly in China. Generally, large-diameter rigid steel piles are used in offshore wind turbine foundations. However, existing design methods for pile foundations with bearing capacity are often based on small-diameter flexible piles and are inapplicable to real situations. Simultaneously, thick layers of clay cover the fields. Therefore, the shear characteristics of pile–clay interface require examination to optimize design methods. A large-scale interface shear test model corresponding to laboratory tests is established using PFC software, and the micro-parameter values are validated. The simulation results well follow the laboratory test results, and the model accurately reflects pile–clay interactions during shearing. Based on the simulation model, the shear characteristics of the steel–clay interface under different normal loads, cyclic load frequencies, and cyclic load amplitudes are analyzed. With an increase in normal load, the shear strength of the steel-clay interface increases gradually, whereas it slightly decreases with an increase in the cyclic load frequency and amplitude; particularly, the effect of amplitude is significant. The mechanical response of clay at a mesoscopic scale is analyzed. Results show that the greater the normal load, the greater the horizontal displacement of particles. However, the influence of cyclic load frequency and amplitude is the opposite: normal load does not change the contact force distribution of soil particles, and clay volume change is determined by the normal load. Thus, this study provides support for bearing capacity prediction of offshore wind farm pile foundations.

A SVM based demand response capacity prediction model considering internal factors under composite program

Demand response capacity is a key reference for demand-side participation in peak regulation and earning profit in the electricity market, and it is influenced by not only external public factors but demand-side internal individual factors. Demand change caused by internal individual reasons cannot be recognized by public features. Single participating in response industrial and commercial consumers cannot balance this change through large numbers of participants, unlike aggregators. The prediction model only considering the external public factors will cause significant errors. However, the actual industrial and commercial data is difficult to obtain because of the cost and privacy, and the simulation platform cannot accurately describe this situation, resulting in the problem being ignored. Therefore, this paper aims to prove the importance of internal factors and proposes an improved method considering the problem. First, based on an actual dataset, the optimal responsive behavior of consumers is modeled to quantify response capacity. Second, a support vector machine (SVM) method is applied to predict response capacity. Finally, combined with raw data, this paper proves that significant errors are caused by internal factors and provides different improvement methods for internal and external operators. The case study indicates that the improved model can provide better performance.

Load-bearing capacity prediction of the orthogonal double corrugated SPSW under combined compression and shear load

The orthogonal double corrugated steel plate shear wall (ODC-SPSW) is composed of two corrugated steel plates placed orthogonally and connected by high-strength bolts at the corrugation valley regions. The load-bearing capacity prediction method of the ODC-SPSW subjected to both axial compression and shear should be investigated for engineering design. Firstly, with considering the embedded ODC-SPSW being simply supported on both sides and fixed on other both sides, its compressive elastic buckling load is derived using the equilibrium method, and the formula of the compressive elastic buckling load with a correction coefficient considering the differences in boundary simplification is corrected and verified through the finite element (FE) elastic buckling analysis to obtain the calculation formula of normalized high-thickness ratio. Additionally, the compression load-bearing capacity of the ODC-SPSW is approximately equal to the sum of the compression load-bearing capacity of the embedded ODC-SPSW and edge members. Finally, the load-bearing capacity of the ODC-SPSW under the combined load of the axial compression and shear is studied, and the design method of compression-shear interactive curves is proposed to guide engineering applications.

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides.

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann-Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol-1 K-1) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures.

Probe maps for the exploration of localized metastable states and prediction of peak load-carrying capacity of axially compressed cylindrical shells

A Machine Learning (ML) based analysis is conducted to find the most influential probing parameter in determining the buckling initiation location by assessing Probe Force–Probe Displacement characteristic curves. This is followed by the construction of Probe maps, which systematically depict the variation of this parameter with the help of numerical simulations incorporating Measured Geometric Imperfections (MGI). The probe maps hence constructed are found to be effective in identifying the critical locations where a single dimple is likely to initiate the buckling, enabling the complete exploration of the critical single dimple states of the structure. The experiments by probing the structure at the critical locations identified using probe maps show accurate prediction of the peak load-carrying capacity. The approach presented in the study may be extrapolated to characterize imperfection-sensitive structures in general.

Predicting the ultimate bearing capacity of spudcan in stiff-over-soft clay based on a failure model varying with soil parameter

A jack-up platform is prone to a ‘punch-through’ accident when working on the stiff-over-soft clay foundation. To assess the risk of punch-through, it is necessary to plot the bearing capacity-penetration curve for evaluating the penetration depth of the pile leg or to calculate the ultimate bearing capacity of the spudcan and compare it with the preload. However, accurate prediction of spudcan bearing capacity is not easy. In this paper, the failure mode about the stiff-over-soft clay under different geological conditions when punch-through occurs is discussed using coupled Eulerian lagrangian (CEL) method, varying the strength ratio between soft and stiff soil layers, the thickness of stiff soil relative to the spudcan diameter, and the non-homogeneity factor of soil strength. Based on the soil failure model with varying soil parameters, a new calculation method for ultimate bearing capacity of spudcan is proposed and then verified against existing centrifuge test results. The results show that the shape of soil shear plane in stiff clay is significantly affected by the strength ratio of soil layer and the non-homogeneity factor of soil strength. And the presented new method whose prediction errors compared with centrifuge test results are less than 5%, has better calculation accuracy than the ISO (International Organization for Standardization) method, which is valuable in guiding the calculation of the ultimate bearing capacity of the spudcan and ensuring the safe operation of the jack-up drilling units on stiff-over-soft clay.

Experimental study on flexural behavior and bending capacity prediction of Q550E steel beams within corroded shear span

An experimental study is carried out to investigate the degradation behavior of Q550E HPS steel beams with corroded shear span. At first, tensile tests of 15 pieces of steel are undertaken to obtain the degradation law of mechanical property parameters. Four steel beams with different corrosion damage levels in shear span are designed by electrochemical accelerated corrosion. The flexural loading tests of corroded steel beams are conducted, and the mechanical response of local corrosion to steel beams behavior is discussed. The necessity of lateral bracing is discussed theoretically. Considering the constitutive model affected by corrosion and the role of stiffeners, a predictive model of bending bearing capacity is proposed. The results show that the increasing corrosion damage will cause the failure position to move from mid-span to shear span, and the failure mode will shift from mid-span compressive flange buckling to bending-shear buckling, and finally appear web buckling. Corrosion damage level higher than 10% will lead to insufficient strain development. The proposed model can predict the flexural capacity and deflection well. Compared with the test results, the bearing capacity and deflection of corroded HPS beams are predicted by the nonlinear iterative method with high accuracy.

Corrosion effects on axial pile capacity

Increase in surface roughness by corrosion processes has long been neglected as potential factor influencing pile setup. However, recently there has been an increasing number of studies who referred pile setups largely or solely to corrosion and sand incrustation. Only limited research has been conducted to assess the potential impacts of corrosion directly on pile capacity development. Therefore, we sampled steel and crust surfaces from a steel monopile having been aged for ∼four years in sand. Surface roughness measurements and interface direct shear testing were performed to quantify changes for friction angles. The impact of friction angle changes on pile capacity were calculated using ICP-05 and UWA-05 for a large- and small-diameter geometry and referenced by field data. We can show that corrosion can significantly contribute to temporal pile capacity gains. Evidence have been found that the maximum and critical interface friction angles evolve differently considering the same changes in roughness. Also, differences in shearing behavior to literature were observed, being potentially a result of the naturally corroded surfaces sheared in our study. A strong, maybe exaggerated sensitivity of the capacity prediction approaches to pile diameter was observed. Effects causing an increase in surface roughness, should be reconsidered as an important factor influencing pile setup.

Confinement-based design and behaviour of concrete-filled stiffened steel tubular square slender columns

This paper presents the details, analysis and results of an investigation into the compressive behaviour of concrete-filled stiffened steel tubular (CFSST) slender columns. These are members comprising an outer steel section with longitudinal stiffeners welded to the interior surface. A finite element (FE) model was developed and validated. An improvement in ultimate strength compared to conventional concrete-filled steel tubular column has been found due to the presence of stiffeners that reduce the width-to-thickness ratio of the steel tube, which in turn increases the effective cross-sectional area of the steel tube. Moreover, it was found that stiffeners increase the lateral confining pressure on the concrete. FE modelling was then employed to conduct a thorough parametric study to investigate the influence of a wide range of parameters. These included the slenderness ratio, concrete compressive strength, as well as the geometry and details of the stiffeners. Both medium-length and long CFSST columns, classified on the basis of slenderness ratio, were carefully studied and discussed, as well as their inherent differences. The strengths predicted from the FE simulations were compared with the design strengths obtained from various international design codes, and it was shown that the design expressions provide very conservative capacity predictions. Accordingly, a new three-stage confinement-based design model was proposed, which takes into account different confining mechanisms of columns based on their slenderness ratio. It is illustrated that this model provides a very accurate prediction of the square CFSST slender columns, and captures the key behavioural characteristics.