Nonlinear Analysis Method Research Articles

Numerical and Analytical Study on the Punching Shear Capacity Prediction for Eccentrically Loaded Flat Slabs with Openings

The punching shear behavior of interior flat slab was examined in this study in the absence of shear reinforcement using numerical and analytical techniques. The effect of sensitive parameters was investigated and their influence was examined and reported including the opening existence and location, loading eccentricity, and slab height. The effect of the reinforcement ratio is neglected in most of the literature models and code provisions with the influence of loading eccentricity being addressed inappropriately. Therefore, this study adopted the nonlinear finite element analysis (NLFEA) method for the numerical part where the flat slab punching shear behavior was studied in detail. Results were compared in their behavior at both cracking and ultimate stages, absorbed energy, elastic and plastic stiffnesses, and general trend lines were provided. The effect of the reinforcement ratio was assessed indirectly with slabs of similar reinforcement area and different slab effective depths (65, 95, 125, and 155) mm where a 25 mm concrete cover was used. In addition, the NLFEA punching shear capacity results were compared with three international codes (American Concrete Institute (ACI318-119), EuroCode (EC2), and Model Code (MC2010)) to highlight their potential predictability and weakness points. It was found that the MC2010 has the most conservative prediction, followed by the EC2 and the ACI318-19 predictions. Therefore, two modification parameters were proposed for the ACI318-19 punching shear capacity calculation with R2 value of 0.98. The modified version of the ACI318-19 was validated using experimental data from the literature proving a high prediction capability.

Constant-ductility inelastic displacement ratio spectra for design of superstructures in seismically isolated buildings

Constant-ductility inelastic displacement ratio (Cμ) spectra can be utilized for the estimation of peak inelastic structural displacements in the seismic design procedure. Currently, suitable analytical estimates of Cμ are lacking for base-isolated buildings with inelastic behavior of superstructures in a beyond design basis earthquake. This paper attempts to fill this research gap and hence conducts parametric studies on the Cμ spectra of base-isolated inelastic superstructures with lead-rubber bearings (LRBs). For that purpose, the inelastic model and equations of motion, boundary conditions of the analytical formulation of Cμ, and controlling parameters for the two-degree-of-freedom (2DF) superstructure-isolator system with LRBs are presented first. Subsequently, by analyzing the parameterized 2DF systems using an ensemble of strong earthquake records representative of a site with stiff soil conditions, the influence of controlling parameters, namely, displacement ductility ratios, superstructure periods, post-yielding stiffness ratios of superstructures, mass ratios, isolation periods and normalized isolator strength, on the mean and dispersion of Cμ are discussed, and the boundary conditions of Cμ are validated based on the calculated data. Comparisons of how the controlling parameters influence the Cμ and earlier investigated CR (i.e. constant-strength inelastic displacement ratio) spectra are also presented. Lastly, a predicting equation with reasonable accuracy is proposed for the mean Cμ using the nonlinear multivariable regression analysis method. The outcome of this study allows the application of displacement-based design via inelastic displacement ratio for new base-isolated structures under beyond design basis earthquakes.

A fast search method of buckling load for telescopic boom structure

The telescopic boom structures are extensively utilized in engineering applications involving large mobile cranes, aerial work platforms of significant height, and similar mechanical devices. These are predominantly fabricated using solid-webbed box types, latticed truss designs, or a combination thereof. Under load, they exhibit pronounced geometric nonlinear effects, with the relationship between load and displacement demonstrating marked nonlinear characteristics. This paper focuses on the geometric nonlinear modeling of slender multi flexible beam structures. The overall structural system is divided into several substructures, and the concept of multi flexible system modeling is borrowed to establish a follow-up connected body foundation on each substructure. By decomposing the node displacement and rotation in the substructure, the large displacement and large rotation of the node are decomposed into rigid body motion of the connected base and small displacement and small rotation relative to the connected base, effectively representing the large displacement and large rotation of the substructure as rigid body motion of the connected base. A modeling method for the geometric nonlinear analysis of slender and flexible multibody beam structures is proposed. By decomposing the nodal displacement and rotation within the substructures, the large displacement and rotation of the nodes are broken down into the rigid body motion of the body-fixed coordinate and small displacement and rotation relative to the body-fixed coordinate. This approach establishes the application conditions for calculating the structure’s virtual power of deformation using traditional beam elements with linear strain. A method for solving the instability load of slender and flexible multibody structures is proposed. This method integrates the characteristics of the system’s nonlinear equilibrium equations with respect to load, by deriving the system equations with respect to a load increment control parameter. This paper converts nonlinear equilibrium equations into first-order ordinary differential equation (ODE). The method proposed in this paper provides a more efficient solution for the buckling load of the telescopic boom. It can be used to systematically and quickly calculate the structure of the telescopic boom.

Simulation of Motion Nonlinear Error Compensation of CNC Machine Tools With Multi-Axis Linkage

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified.

Automated recognition of mental cognitive workload through nonlinear EEG analysis

Nowadays, with the remarkable advancements in detection instruments and artificial intelligence, there has been extensive utilization of human mental state monitoring in various domains. Few studies have explored how nonlinear analysis methods can detect cognitive workload despite the complex nature of EEG signals and advancements in signal processing techniques. In addition, the fuzziness of human mental conditions makes the need to use fuzzy engineering tools tangible in this field. Therefore, this investigation aimed to develop a decision support algorithm to improve previous efforts for the classification of task EEG and resting through machine learning algorithms. Various nonlinear features were calculated from all 19 EEG channels: Hurst exponent, Lempel–Ziv complexity, detrended fluctuation analysis, Higuchi fractal dimension, Katz fractal dimension, permutation entropy, singular value decomposition entropy, Petrosian fractal dimension, sample entropy, and Lyapunov exponent. During the classification step, a newly developed EPC-FC (Expert per Class Fuzzy Classifier) is introduced, utilizing an ensemble framework with specialized sub-classifiers for identifying a particular condition. By training sub-classifiers with the negative correlation learning (NCL) approach, the EPC-FC is designed to be exceptionally adaptable. Additionally, the separation of sub-classifiers within each class provides versatility and clarity to the system’s design. The proposed approach based on fuzzy systems and nonlinear analyses was applied to EEG data for mental workload recognition, which provides an excellent accuracy of 98.50% and an F1-score of 98.56% which is much higher than previous findings in this field. Also, the obtained results indicate that utilizing the proposed EPC-FC classifier maintains a consistently high accuracy exceeding 90% across various levels of SNRs. The obtained results proved the high potential of nonlinear analysis to detect cognitive states of the brain, which is consistent with the nonlinear and fuzzy nature of EEG data. Other nonlinear approaches should be considered for future studies to improve the current results.

Exploration on the improvement of EEG dataset collection and preprocessing methods based on neuroscience theory

This paper analyzes current methods of EEG dataset collection and preprocessing within the framework of neuroscientific theories and proposes potential improvements. Existing datasets are limited because they primarily capture specific stages of neural activity during continuous recitation, missing other crucial stages of language generation. This limitation impacts the performance of deep learning models used for EEG data classification. By examining the multi-stage process of language formation, the paper highlights the need for data collection methods that encompass all stagesconceptualization, grammatical encoding, lexicalization, morphological and phonological encoding, and articulation. The review also discusses the benefits of using nonlinear dynamic system analysis methods over traditional covariance or cross-correlation matrices to better capture the complexities of EEG data. Finally, it suggests treating EEG data at various stages of language formation as multimodal data, as a strategy to improve the comprehensiveness and practicality of EEG datasets, thereby enhancing the accuracy and practical application of EEG-based brain-computer interfaces.

'Knock down the brain': a nonlinear analysis of electroencephalography to study the effects of sub-concussion in boxers.

Boxing is associated with a high risk of head injuries and increases the likelihood of chronic traumatic encephalopathy. This study explores the effects of sub-concussive impacts on boxers by applying both linear and nonlinear analysis methods to electroencephalogram (EEG) data. Twenty-one boxers were selected (mean ± SD, age 28.38 ± 5.5 years; weight 67.55 ± 8.90 kg; years of activity 6.76 ± 5.45; education 14.19 ± 3.08 years) and divided into 'beginner' and 'advanced' groups. The Montreal Cognitive Assessment and the Frontal Assessment Battery were administered; EEG data were collected in both eyes-open (EO) and eyes-closed (EC) conditions during resting states. Analyses of EEG data included normalized power spectral density (nPSD), power law exponent (PLE), detrended fluctuation analysis and multiscale entropy. Statistical analyses were used to compare the groups. Significant differences in nPSD and PLE were observed between the beginner and advanced boxers, with advanced boxers showing decreased mean nPSD and PLE (nPSD 4-7 Hz, p = 0.013; 8-13 Hz, p = 0.003; PLE frontal lobe F3 EC, p = 0.010). Multiscale entropy analysis indicated increased entropy at lower frequencies and decreased entropy at higher frequencies in advanced boxers (F3 EC, p = 0.024; occipital lobe O1 EO, p = 0.029; occipital lobe O2 EO, p = 0.036). These changes are similar to those seen in Alzheimer's disease. Nonlinear analysis of EEG data shows potential as a neurophysiological biomarker for detecting the asymptomatic phase of chronic traumatic encephalopathy in boxers. This methodology could help monitor athletes' health and reduce the risk of future neurological injuries in sports.

Static analysis using flexibility disassembly perturbation for material nonlinear problem with uncertainty

Nonlinear finite element analysis of large-scale structures usually requires a lot of calculation cost, because it is necessary to repeatedly inverse the modified stiffness matrix caused by nonlinearity in the calculation process. When considering the uncertainty in materials, the calculation of the nonlinear analysis will be more unbearable. To improve the computational efficiency, this work develops a new method for nonlinear analysis with material uncertainty based on flexibility disassembly perturbation (FDP) approach. The FDP is an algorithm that can quickly calculate the inverse of a stiffness matrix. The basic idea of the proposed method is to introduce the FDP formula into Newton-Raphson iteration method to accelerate the nonlinear iterative calculation. Three numerical examples, one statically determinate structure and two statically indeterminate structures, are used to verify the accuracy and efficiency of the proposed method. The results show that the calculation time of the proposed method is far less than that of the existing complete analysis and combined approximation algorithms. In terms of computational accuracy, for statically determinate structures, the proposed algorithm can obtain exact solutions that are identical to the complete analysis results, while for statically indeterminate structures, the proposed algorithm can obtain approximate solutions that are very close to the complete analysis results.

The Detailed Axial Compression Behavior of CFST Columns Infilled by Lightweight Concrete

The utilization of lightweight aggregate concrete (LWC) plays a major role in reducing the self-weight of CFST (concrete-filled steel tube) columns, which is reflected in the behavior of the structural system. This paper aims to investigate the characteristics of lightweight concrete-filled steel tubular (LWCFST) columns under an axial compressive load, using a total of (48) LWCFST column models. The simulated models were divided into four groups with different concrete compressive strength, length-to-diameter ratios (L/D), and diameter-to-thickness ratios (D/t). Four concrete compressive values were examined (30, 40, 50, and 60) MPa, three length-to-diameter ratios short (L/D = 3), medium (L/D = 6), and long (L/D = 9), and four diameter-to-thickness ratios (36, 31, 26, and 21). The method of nonlinear finite element analysis (NLFEA) was used to fulfill the objective of this study where results were presented as graphical plots between the compressive loading versus the axial and lateral strains along with the failure modes. In addition, the results were compared with the AISC360-16 and EC4 codes predictions to examine their applicability on the LWCFST columns where the AISC was overpredicted in most cases with higher percentages under lower (L/D) values, whereas the EC2 was underestimated in most cases with high percentages up to 28%, which become closer to the NLFEA predictions at higher (L/D) values. It has been revealed that the utilization of steel tubes significantly improves the LWCFST column’s mechanical performance, ductility, compressive strength, and toughness. Moreover, the structural behavior of the LWCFST columns and their associated failure modes was found to be highly affected by the geometrical properties of the CFST column (i.e., L/D ratio and D/t ratio) where specimens with small tube thickness show bad behavior. Finally, the utilization of high-strength concrete has a favorable performance compared to the utilization of thick steel tubes.

Seismic Performance of Reinforced Concrete Frames on Sloped Ground Using Non-Linear Static Pushover Analysis

Nepal, renowned for its majestic Himalayan Ranges, is also highly vulnerable to seismic activity. Buildings on hill slopes often endure more significant damage during earthquakes than those on flat terrain. Traditional seismic analyses of buildings typically employ linear-static and dynamic methods, which might not fully represent the building's behavior under actual seismic conditions. This research addresses this gap by assessing the seismic performance of buildings on hill slopes using non-linear static analysis techniques. Specifically, the study evaluates the seismic response of 5-story buildings with stepback and stepback-setback configurations on slopes of 15°, 30°, and 45°. This evaluation is conducted through non-linear static analysis, also known as Pushover Analysis, using ETABSv20 software. Key performance metrics analyzed include the building's period, topstorey displacement, and base shear. The findings reveal that buildings with stepbacksetback configurations demonstrate superior seismic performance on the sloping ground compared to those with stepback configurations. This study underscores the importance of employing non-linear analysis methods to better understand and improve seismic resilience in hilly terrains.

Нелинейные главные компоненты биохимического профиля молочного скота пяти пород

Abstract. The purpose of this study was to identify and explain patterns of biochemical parameters in dairy cows, taking into account the characteristics of their breed. Methods. A comparison was made of samples of animals of five breeds (Holstein, Tagil, Suksun, Sychevsk, Istoben) according to 17 biochemical parameters. The total number of animals studied was 407. To analyze the obtained results, statistical analysis methods were used, including the method of nonlinear principal component analysis using the CATPCA (Categorical PCA) algorithm. Scientific novelty. The chosen method made it possible to summarize a large number of biochemical indicators and identify processes in which the studied groups of cows differed to a greater or lesser extent. Results. Five principal components were identified and interpreted, explaining a total of 67.4 % of the total variance. Some observed patterns may indicate the development of pathological conditions in animals. Similar biochemical patterns were observed, on the one hand, in cows of the Tagil, Holstein and Suksun breeds, and on the other, in the Istoben and Sychev breeds. The animals of the Suksun breed were closest to the physiological norm. More pronounced changes associated with a negative energy balance were observed in Holstein cows.

A new strategy for manufacturing, modeling, and optimization of 3D printed polylactide based on multiple nonlinear neuro regression analysis and stochastic optimization methods

Although there are many studies related to design, modeling, and optimization of fused deposition modeling (FDM) process parameters in the literature, the absence of a systematic approach to increase the reliability of model selection and optimization results is an important shortcoming that must be addressed. To make up for this deficiency, a new strategy was proposed to obtain desired quality on mechanical properties by adjusting FDM process parameters. This attempt involves manufacturing, modeling, and optimization point of view. The D-optimal method was employed to form experiment set, including process parameters. A hybrid approach neuro regression combining artificial neural network (ANN) and regression analysis together was used for modeling of FDM process. The most significant advantage of the neuro-regression approach compared to ANN is that the mathematical models can be used directly without needing any transformation. This is not possible in neural networks and, therefore, significantly limits and complicates the use of models obtained using ANN. The present study aims at optimization of the FDM process parameters, including infill density, infill pattern, layer thickness, and print speed on ultimate strength, fracture strength, and fracture strain for polylactide (PLA). In this regard, modified versions of the optimization algorithms Differential Evolution, Nelder Mead, and Simulated Annealing were used to find the best or elite designs. Linear or nonlinear models consisting of polynomial, trigonometric, and logarithmic expressions and their hybrid forms were employed to define the strength and strain behavior of PLA. It is concluded that (a) implementations of the optimization algorithms provide a 19% improving the minimum strain value if it is compared with the experimental results, (b) infill pattern types ( x2) were found as honeycomb, triangle, and cubic for the designs in terms of maximum fracture strength, minimum strain, and maximum ultimate tensile strength respectively, (c) many alternatives near optimum local designs could be obtained based on Nelder Mead algorithm for fracture strength and ultimate strength parameters. Thus, this allows work in a wide range of applications without depending on a single result for production.

Nonlinear dynamic characteristics analysis method of planetary gear train torsional vibration considering meshing oil film force

Planetary gear train is an important part of automatic transmission. The oil film between teeth is one of the key factors affecting the torsional vibration of planetary gear train. Selecting the appropriate oil film stiffness is of great significance for analyzing the nonlinear dynamic characteristics of planetary gear train. A method for analyzing the nonlinear dynamic characteristics of planetary gear system torsional vibration considering the oil film force between teeth is proposed in this paper. A 3-DOF torsional vibration model of planetary gear train considering oil film is established. In this model, the oil film between teeth is equivalent to a time-varying nonlinear spring-damper element, and nonlinear factors such as backlash and meshing error are also considered. The effects of meshing oil film thickness on oil film stiffness and damping are analyzed, and the torsional vibration characteristics of planetary gear train under different meshing stiffness, meshing damping ratio, and rotational speed are analyzed based on bifurcation diagrams. The results show that in one meshing cycle, the meshing oil film thickness first becomes larger and then becomes smaller. When the meshing oil film thickness increases, the meshing oil film stiffness and the equivalent meshing oil film damping become smaller. The torsional vibration of planetary gear train can be effectively reduced by increasing meshing stiffness, damping ratio, rotational speed within a certain range. This method provides technical support for reducing torsional vibration of planetary gear train and realizing stable transmission of planetary gear train.

Mode selection and combination rules of energy-based multimodal pushover method for simply supported beam bridges

The energy-based method (EBM) has been improved as Energy-based Multimodal Pushover Method (EMPM), and is introduced and verified to be effective and convenient for evaluate the seismic performance of the simply supported beam bridges. This paper mainly focuses on the research of vibration mode selection and combination rules of EMPM. Firstly, the longitudinal vibration modes of simply supported beam bridge are categorized into five cases based on mode shapes, and the case definition is emphasized. Then, corresponding to each case, the Pushover loading force are established, and the modal energy, modal displacement, and modal energy capability curve are proposed. After that, the energy-based demand curve and the corresponding energy coefficient for each case is recommended. Finally, only the mode with maximum mode participation factor (MPF) of each case is suggested to take part in the calculation of the seismic response with the proposed mode participation factor combination rule. Case studies of a practice bridge under Chi-Chi and El Centro earthquake waves are used to illustrate the effectiveness and accuracy of the proposed mode selection and combination rules. The study found that the EMPM with the proposed rules could effectively and rapidly assessing the seismic performance of the simply supported beam bridge, resulting in a 70 % decrease in time consumption compared with the Nonlinear Time History Analysis Method (NTHAM). Compared with the CQC and SRSS combination results, the proposed Mode Participation Factor (MPF) combination rule reduced the maximum relative deviation from 39.4 % to 21.09 % between EMPM and NTHAM for peak displacement. Further research showed that the proposed mode selection approach only uses modes with the highest MPF in each case, decreased the number of modes involved more than 50 % and the result accuracy remained almost identical.

Multi-Load Topology Optimization Design for the Structural Safety Maintenance of Low- and Intermediate-Level Radioactive Waste Packaging Containers in the Case of a Collision.

This paper presents an optimized design approach using nonlinear dynamic analysis and finite element methods to ensure the structural integrity of square-shaped containers made from ductile cast iron for intermediate- and low-level radioactive waste packaging. Ductile cast iron, with its spherical graphite structure, effectively distributes stress throughout the material, leading to a storage capacity increase of approximately 18%. Considering the critical need for containers that maintain integrity under extreme conditions like earthquakes, the design focuses on mitigating stress concentrations at the corners of square structures. Nonlinear dynamic analyses were conducted in five drop directions: three specified by ASTM-D5276 standards and two additional directions to account for different load patterns. Fractures were observed in four out of the five scenarios. For each direction where fractures occurred, equivalent loads causing similar displacement fields were applied to linear static models, which were then used for multi-load topology optimization. Three optimized models were derived, each increasing the volume by 1.4% to 1.6% compared to the original model, and the design that best met the structural integrity requirements during drop scenarios was selected. To further enhance the optimization process, weights were assigned to different load conditions based on numerical analysis results, balancing the impact of maximum stress, average stress, and plastic deformation energy. The final model, with its increased storage capacity and enhanced structural integrity, offers a practical solution for radioactive waste management, overcoming limitations in previous designs by effectively addressing complex load conditions.

Spray swirling flame dynamic under combined modulation of air and fuel

Combustion instability is still a main challenge in developing advanced gas turbine combustors. It is essential to conduct instability control once it occurs. In this paper, liquid fuel modulation by a high-speed on/off valve is used to control flame oscillation generated by loudspeakers. Spray swirling flame dynamic under the combined modulation of air and fuel is experimentally studied. Flame dynamical characteristics and coherent structures at different excitation phase delays are discussed with nonlinear time-series analysis and proper orthogonal decomposition method. Experimental results show flame heat release rate fluctuation is decreased by 86% at 40° while flame instability is intensively enhanced at 220°. Phase portraits and recurrence plots show the flame is in a chaotic oscillation with low amplitude aperiodic oscillations at 40° while it suffers from the largest fluctuation at 220°. Difference in flame dynamic is caused by the destructive (out of phase) and constructive (in phase) interference of heat release rate fluctuations induced by air excitation and fuel modulation. Flame returns back to its nature dynamical behavior under out-of-phase modulation. Longitudinal modes of the spray flame are enhanced while the spiral modes are inhibited under in-phase modulation.

Seismic performance and design of an innovative structure with controlled rocking shear wall using ring springs

The application of ring springs in civil engineering, aimed at improving the seismic behavior of structures, has been largely overlooked despite their many advantages. These springs are highly durable, heat-resistant, low-maintenance, and self-centering, meaning that they withstand large deformations while always returning to their original state. Structures equipped with properly designed ring springs survive earthquakes without suffering major damage or plastic deformation, making them an excellent investment in terms of both economic efficiency and sustainable resource use. This paper explores a new use for ring springs in earthquake-resistant structures, with a focus on their implementation into controlled rocking shear walls. A parametric study utilizing the Non-Linear Time History Analysis method was conducted on a simplified model to investigate the rocking behavior and derive a design methodology based on the obtained results. A comparative study was conducted on elastic, non-linear, and rocking shear walls to demonstrate the feasibility and design principles of the rocking systems. Furthermore, the results of the comparative studies provided evidence supporting the efficacy of incorporating ring spring dampers into the rocking systems. The controlled rocking shear wall demonstrated elastic behavior during seismic loading, indicating that the addition of ring spring dampers was effective in enhancing its performance. On the other hand, the conventionally designed numerical model exhibited nonlinear behavior at its base. The developed design procedure ultimately enables the practical application of controlled rocking shear walls using ring spring dampers in earthquake engineering.

The ultimate capacity of geopolymer recycled aggregate concrete filled steel tubular columns: Numerical and theoretical study

Concrete-filled steel tubular (CFST) columns have been extensively used due to their numerous advantages including high load-bearing capacity, stiffness, energy absorption, and ductile failure mode. Human awareness toward environmental problems is increasing and research on geopolymer recycled aggregate concrete (GRAC) has gained much attention as a green and sustainable material. However, there is little experimental and numerical research on the behavior of CFST columns with the GRAC as an infill material with the available design codes not yet modified for addressing the utilization of unconventional concrete mixes, such as the GRAC. This research introduces a critical modification of the ACI code provisions using the nonlinear finite element analysis method (FEA) on the behavior of square GRACFST columns under axial compression where the proposed modification was verified using experimental data from the literature. The influence of the length-to-width ratio, width-to-thickness ratio, geopolymer concrete strength, and the recycled aggregate replacement ratio was investigated. Results show that the outward local buckling was the primary failure mode for both short and slender columns. Considerable improvements were observed in the column's load-bearing capacity, initial stiffness, ductility, and energy absorption by (81.0, 101.9, 46.5, and 87.8)%, respectively, achieved upon increasing the steel tube thickness from 3 mm to 9 mm. However, the increase in length-to-width ratio negatively affects the energy absorption by 39 % while the ductility was reduced up to 20.4 %. The predictability of the AISC360-16, ACI318-19, and BS5400-5 codes on the capacity of GRACFST columns was evaluated where conservative predictions were guaranteed with the closest one recorded for the ACI318-19 code with a 24.5 % overestimation percentage. Finally, a modification was proposed to the ACI code prediction, taking into account the inclusion of recycled aggregate and geopolymer concrete with only a 2 % average error.

Nonlinear seismic assessment for nuclear power plants with structural-geotechnical hybrid isolation strategy

Improving the seismic resilience of nuclear power plants (NPP) to withstand super-strong earthquakes is critical for ensuring nuclear safety. Seismic isolation can effectively enhance the structural seismic margins and has significant potential for development in NPPs. Although three-dimensional (3D) base isolation can effectively isolate tri-directional ground motion, it also faces challenges such as the limitations of horizontal and vertical displacements of the isolation layer under super-strong earthquakes. In this paper, a novel hybrid passive control strategy that combines 3D base isolation with geotechnical seismic isolation (GSI) systems is proposed. Furthermore, a nonlinear soil-structure interaction analysis method is developed and the program is validated using numerical examples. Finally, a nonlinear dynamic analysis of a large-scale nuclear island building at a non-bedrock site is conducted to investigate the seismic mitigation effects of a hybrid isolation system under super-strong earthquakes. The research findings demonstrate a significant improvement in the isolation effectiveness of the proposed strategy compared to 3D base isolation, which further reduces the tri-directional seismic responses of NPPs while also exhibits the ability to control the deformation in the isolation layer. The proposed hybrid isolation system can significantly improve the ability of NPPs to withstand super-strong earthquakes and provide support for the hybrid seismic isolation design of NPPs.

Temperature Control of Concrete Structure in Bridge Construction by Renewable Energy Technology

In this study, the classical display nonlinear dynamic analysis method was used to detect the temperature of concrete structures in bridge construction. Firstly, two different bridge construction test methods, the Winfrith subfunction and CSCM subfunction, were selected to detect the nonlinear temperature of the simulated concrete, and the temperature of the bridge concrete structure and the residual velocity of solidification were detected and analyzed by combining the nonlinear dynamic algorithm for data processing. The results show that the data of the nonlinear dynamic test in this paper agree with the actual requirements, and the accuracy rate is about 90~95%. In addition, the Winfrith subfunction calculates the data of the large strain force of photovoltaic temperature control energy on concrete. It identifies the nonlinear characteristics of high strain rate, especially the effect of photovoltaic heating, battery stable power supply heating, and the effect of wind on water vapor dissipation, which further proves that renewable energy technology can monitor the temperature of concrete structures in bridge construction, and effectively detect and identify concrete problems to support the construction and later use of bridge concrete.