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Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network 认领 引用 被引量：4
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作者 Xiao-rui Wang Yuan-han Wang Xiao-feng Jia 《Journal of Pharmaceutical Analysis》 CAS 2009年第1期22-30,共9页
Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretica... Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering.During design,it is a frequent practice,therefore,to give recommended values by analog based on experience.It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying,expressing and coping with such complex non-linear relationships.The parameters can be verified by searching the optimal network structure,using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results.In the current paper,the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua(FLAC3D.The high non-linearity,network reasoning and coupling ability of the neural network are employed.The output vector required of the training of the neural network is obtained with the numerical analysis software.And the overall space search is conducted by employing the Adaptive Immunity Algorithm.As a result,we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum.At the same time,the computing speed and efficiency are increased as well.Further,in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project.The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data.This is of practical significance to the appraisal of stability and informationization design of the surrounding rock. 展开更多
关键词 adaptive immunity algorithm BP neural network physical and mechanical parameters surrounding rock direct-back analysis
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Research on Application of Enhanced Neural Networks in Software Risk Analysis 认领 引用
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作者 Zhenbang Rong Juhua Chen +1 位作者 Mei Liu Yong Hu 《南昌工程学院学报》 CAS 2006年第2期112-116,121,共5页
This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity ... This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model. 展开更多
关键词 software risk analysis principal components analysis back propagation neural networks genetic algorithms
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A Novel Remote Sensing Signal De-noising Algorithm based on Neural Networks and Tensor Analysis 认领 引用
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作者 Wang Wei 《International Journal of Technology Management》 2016年第9期26-28,共3页
. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet c... . This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance. 展开更多
关键词 Remote Sensing De-noising Algorithm Neural Networks Tensor Analysis
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Hybrid deep learning and isogeometric analysis for bearing capacity assessment of sand over clay 认领 引用 被引量：1
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作者 Toan Nguyen-Minh Tram Bui-Ngoc +2 位作者 Jim Shiau Tan Nguyen Trung Nguyen-Thoi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第8期5240-5265,共26页
In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.... In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements. 展开更多
关键词 UB limit analysis Isogeometric analysis(IGA) Hybrid deep neural network Whale optimization algorithm
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Gray relational analysis and SBOA-BP for predicting settlement intervals of high-speed railway subgrade 认领 引用
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作者 Quanpeng He Shaoyuan Li 《Railway Sciences》 2025年第2期199-212,共14页
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s... Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications. 展开更多
关键词 Gray relational analysis Secretary bird optimization algorithm Backpropagation neural network Subgrade settlement Interval prediction
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Evaluation of Stiffened End-Plate Moment Connection through Optimized Artificial Neural Network 认领 引用 被引量：1
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作者 Mehdi Ghassemieh Mohsen Nasseri 《Journal of Software Engineering and Applications》 2012年第3期156-167,共12页
This study involves the development of an analytical model for understanding the behavior of the extended, stiffened end-plate moment connections with eight high strength bolts. Modeling of the connection as an assemb... This study involves the development of an analytical model for understanding the behavior of the extended, stiffened end-plate moment connections with eight high strength bolts. Modeling of the connection as an assemblage of finite elements (FE) used for load deformation analysis, with material, and contact nonlinearities are developed. Results from the FE mathematical model are verified with results from the ANSYS computer program as well as with the test results. Sensitivity and feasibility studies are carried out. Significant geometry and force related variables are introduced;and by varying the geometric variables of the connections within a practical range, a matrix of test cases is obtained. Maximum end-plate separation, maximum bending stresses in the end-plate, and the forces from the connection bolts for these test cases are obtained. From the FE analysis, a database is produced to collect results for the artificial neural network analysis. Finally, salient features of the optimized Artificial Neural Network (ANN) via Genetic Algorithm (GA) analysis are introduced and implemented with the aim of predicting the overall behavior of the connection. 展开更多
关键词 End-Plate Moment Connection Finite Element Method Artificial Neural Network Sensitivities Analysis Genetic Algorithm
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Advanced multiple response surface method of sensitivity analysis for turbine blisk reliability with multi-physics coupling 认领 引用 被引量：11
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作者 Zhang Chunyi Song Lukai +2 位作者 Fei Chengwei Lu Cheng Xie Yongmei 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2016年第4期962-971,共10页
To reasonably implement the reliability analysis and describe the significance of influencing parameters for the multi-failure modes of turbine blisk, advanced multiple response surface method (AMRSM) was proposed for... To reasonably implement the reliability analysis and describe the significance of influencing parameters for the multi-failure modes of turbine blisk, advanced multiple response surface method (AMRSM) was proposed for multi-failure mode sensitivity analysis for reliability. The mathematical model of AMRSM was established and the basic principle of multi-failure mode sensitivity analysis for reliability with AMRSM was given. The important parameters of turbine blisk failures are obtained by the multi-failure mode sensitivity analysis of turbine blisk. Through the reliability sensitivity analyses of multiple failure modes (deformation, stress and strain) with the proposed method considering fluid-thermal-solid interaction, it is shown that the comprehensive reliability of turbine blisk is 0.9931 when the allowable deformation, stress and strain are 3.7 x 10(-3) m, 1.0023 x 10(9) Pa and 1.05 x 10(-2) m/m, respectively; the main impact factors of turbine blisk failure are gas velocity, gas temperature and rotational speed. As demonstrated in the comparison of methods (Monte Carlo (MC) method, traditional response surface method (RSM), multiple response surface method (MRSM) and AMRSM), the proposed AMRSM improves computational efficiency with acceptable computational accuracy. The efforts of this study provide the AMRSM with high precision and efficiency for multi-failure mode reliability analysis, and offer a useful insight for the reliability optimization design of multi-failure mode structure. (C) 2016 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. 展开更多
关键词 Advanced multiple response surface method Artificial neural network Intelligent algorithm Multi-failure mode Reliability analysis Turbine blisk
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Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays 认领 引用 被引量：6
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作者 Chengyu Xie Hoang Nguyen +1 位作者 Yosoon Choi Danial Jahed Armaghani 《Geoscience Frontiers》 SCIE CAS CSCD 2022年第2期34-51,共18页
Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures... Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R2)of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R2of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R2in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy. 展开更多
关键词 Diaphragm wall deflection Braced excavation Finite element analysis Clays Meta-heuristic algorithms Functional linked neural network
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Semi-autogenous mill power prediction by a hybrid neural genetic algorithm 认领 引用 被引量：7
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作者 Hoseinian Fatemeh Sadat Abdollahzadeh Aliakbar Rezai Bahram 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第1期151-158,共8页
There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill l... There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters. 展开更多
关键词 semi-autogenous mill mill power prediction sensitivity analysis artificial neural network genetic algorithm
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A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis 认领 引用 被引量：6
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作者 Ce Zhang Azim Eskandarian 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第7期1222-1242,共21页
The driver’s cognitive and physiological states affect his/her ability to control the vehicle.Thus,these driver states are essential to the safety of automobiles.The design of advanced driver assistance systems(ADAS)... The driver’s cognitive and physiological states affect his/her ability to control the vehicle.Thus,these driver states are essential to the safety of automobiles.The design of advanced driver assistance systems(ADAS)or autonomous vehicles will depend on their ability to interact effectively with the driver.A deeper understanding of the driver state is,therefore,paramount.Electroencephalography(EEG)is proven to be one of the most effective methods for driver state monitoring and human error detection.This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades.First,the commonly used EEG system setup for driver state studies is introduced.Then,the EEG signal preprocessing,feature extraction,and classification algorithms for driver state detection are reviewed.Finally,EEG-based driver state monitoring research is reviewed in-depth,and its future development is discussed.It is concluded that the current EEGbased driver state monitoring algorithms are promising for safety applications.However,many improvements are still required in EEG artifact reduction,real-time processing,and between-subject classification accuracy. 展开更多
关键词 Advanced driver assistance systems(ADAS) data analysis electroencephalography(EEG) intelligent vehicles machine learning algorithms neural network.
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Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm 认领 引用 被引量：2
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作者 Zan Zhou Thomas Man-Hoi Lok Wan-Huan Zhou 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2024年第2期345-358,共14页
Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known bef... Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion. 展开更多
关键词 surface wave inversion analysis shear-wave velocity profile deep neural network genetic algorithm
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Sentiment Analysis on Social Media Using Genetic Algorithm with CNN 认领 引用 被引量：1
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作者 Dharmendra Dangi Amit Bhagat Dheeraj Kumar Dixit 《Computers, Materials & Continua》 SCIE EI 2022年第3期5399-5419,共21页
There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites.Today,customers throughout the world share their points of view on all kinds of topics... There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites.Today,customers throughout the world share their points of view on all kinds of topics through these sources.The massive volume of data created by these customers makes it impossible to analyze such data manually.Therefore,an efficient and intelligent method for evaluating social media data and their divergence needs to be developed.Today,various types of equipment and techniques are available for automatically estimating the classification of sentiments.Sentiment analysis involves determining people’s emotions using facial expressions.Sentiment analysis can be performed for any individual based on specific incidents.The present study describes the analysis of an image dataset using CNNswithPCA intended to detect people’s sentiments(specifically,whether a person is happy or sad).This process is optimized using a genetic algorithm to get better results.Further,a comparative analysis has been conducted between the different models generated by changing the mutation factor,performing batch normalization,and applying feature reduction using PCA.These steps are carried out across five experiments using theKaggledataset.The maximum accuracy obtained is 96.984%,which is associated with the Happy and Sad sentiments. 展开更多
关键词 Sentiment analysis convolutional neural networks facial expression genetic algorithm
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Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm 认领 引用 被引量：1
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作者 Yanhua Lu Xuehui Gong Andrew Byron Kipnis 《Computers, Materials & Continua》 SCIE EI 2022年第9期5481-5497,共17页
Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by us... Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University. 展开更多
关键词 Energy consumption analysis model BP neural network genetic algorithm
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Rheological Properties of Solid Rocket Propellants Based on Machine Learning 认领 引用
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作者 Minghai Zheng Zhaoxia Cui +1 位作者 Jiang Liu Jianjun Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期431-455,共25页
To accurately depict the strong nonlinear relationship between the viscosity of propellant slurry and shear rate,premix time,and temperature,and to improve the prediction accuracy,based on the sample preparation and e... To accurately depict the strong nonlinear relationship between the viscosity of propellant slurry and shear rate,premix time,and temperature,and to improve the prediction accuracy,based on the sample preparation and experimental measurement of a certain type of propellant,viscosity data under multiple working conditions were obtained as the basic data for the research.By comparing typicalmodels such as support vector regression and random forest,it was found that although the traditional BP neural network was superior to the both,its accuracy was still insufficient.Based on this,a BPmodel co-optimized by the SparrowSearch Algorithm(SSA)and theGenetic Algorithm(GA)is proposed.The global search of SSA and the local convergence of GA are utilized to conduct dual optimization of the initial weights and thresholds of the BP network,and the training is completed based on themeasured shear rate,temperature and time data.Further,the Box-Behnken response surface design is adopted to transformthe output of the neural network into a quadratic explicit function of viscosity and multiple factors.The results show that the SSA-GABP model achieves a determination coefficient of R2=0.948,compared with R2=0.628 for the traditional BP neural network,while the root mean square error(RMSE)is reduced from 1093.99 to 154.75.Within the key pouring viscosity range of 200–600 Pa⋅s,the prediction deviation remains within±5%,and the overall prediction variance is improved by more than 100%.The polynomial quantification obtained from the response surface reveals the dominant role of shear rate and its interaction with temperature and time.The fitting curve is more in line with the experimental trend than the traditional constitutive model.The constructed explicit function can be directly embedded in Computational Fluid Dynamics(CFD)for pouring process simulation and has good engineering application value. 展开更多
关键词 Non-Newtonian fluid kinematic viscosity neural network optimization algorithm response surface analysis
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New Approaches for Image Compression Using Neural Network 认领 引用 被引量：1
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作者 Vilas H. Gaidhane Vijander Singh +1 位作者 Yogesh V. Hote Mahendra Kumar 《Journal of Intelligent Learning Systems and Applications》 2011年第4期220-229,共10页
An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techn... An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications. 展开更多
关键词 Image Compression Feed Forward Back Propagation Neural Network Principal Component Analysis (PCA) Levenberg-Marquardt (LM) Algorithm PSNR MSE
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Failure Analysis of Austenitic Stainless Steel Implant Screws and Prospection of Chemical Composition Using Artificial Intelligence 认领 引用
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作者 Alfonso Monzamodeth Román-Sedano Bernardo Campillo +1 位作者 Fermín Castillo Osvaldo Flores 《World Journal of Engineering and Technology》 2022年第1期98-118,共21页
In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screw... In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screws were extracted due to the observation of slight superficial red rust colorizing on one of the screw implants, visual evidence of probable screw rusting. From the same implant, another screw was extracted simultaneously without visual evidence of rusting. In order to characterize and analyze the different behavior of both screws, the chemical composition was characterized by atomic absorption and energy dispersive X-ray spectroscopy (EDS) coupled to a scanning electron microscope (SEM). Also, the screws were studied by metallography, optical microscopy (OM), Vickers microhardness tests, and SEM analysis. On the other hand, a prospection for alloy chemical composition limits of these types of implants was performed based on the Schaeffler-Delong diagram and the ASTM F-138 standard. To analyze the effect of the chemical composition, heat treatment, microstructure, pitting resistance equivalent number (PRE) and stacking fault energy (SFE), a genetic algorithm (GA) and an artificial neural network (ANN) were used. In accordance with the elemental analysis, the surgical screws do not fulfill the ranges of the chemical composition established by the ASTM F-138 standard. Furthermore, there were found differences between the microstructures of the screws. In regard to the prospection, the results of GA and ANN support the proposed chemical composition region on the Schaeffler-Delong diagram. The corrosion failure was associated with severe plastic deformation and the presence of precipitates. The proposal can minimize the cause of failures in these types of austenitic stainless steel implants. 展开更多
关键词 Implant Screw Failure Analysis Genetic Algorithm Artificial Neural Network Austenitic Stainless Steel
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基于智能优化与特征选择的埋地管道腐蚀深度预测研究 认领 引用
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作者 梁瑞 王妮鹏 +3 位作者 周文海 李楷 宋彦宏 胡才智 《安全与环境学报》 CAS CSCD 北大核心 2026年第7期2560-2572,共13页
针对现有埋地管道点蚀预测模型在特征工程与物理解释性方面存在的双重局限性,研究提出一种融合智能优化与机理驱动分析的预测新范式。该方法通过优化算法提升神经网络性能,并利用特征选择,从复杂的土壤环境数据中识别关键腐蚀驱动因素,... 针对现有埋地管道点蚀预测模型在特征工程与物理解释性方面存在的双重局限性,研究提出一种融合智能优化与机理驱动分析的预测新范式。该方法通过优化算法提升神经网络性能,并利用特征选择,从复杂的土壤环境数据中识别关键腐蚀驱动因素,有效地规避了信息冗余。结果显示,所提模型的均方误差(Mean Squared Error,MSE)低至0.058 75,预测精度显著提高。此外,可解释性分析证实,该模型的内部决策逻辑与腐蚀理论高度吻合,其将服役年限、土壤pH值和关键侵蚀离子作为核心预测依据,展现出识别冗余特征的学习能力。研究提供了一个兼具高精度、强鲁棒性与物理解释性的预测工具,为实现精准、可靠的管道完整性管理与预测性维护提供了强大的决策支持。 展开更多
关键词 安全工程 管道腐蚀 特征选择 可解释性分析 神经网络 优化算法
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基于机器学习的水稻始穗期预测方法 认领 引用
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作者 任义方 朱凤 陈思宁 《中国农业气象》 CSCD 2026年第4期558-571,共14页
水稻始穗期是稻曲病、穗稻瘟、纹枯病等病害病菌侵入谷粒等器官造成稻米品质降低产量减少的关键期。为提高水稻病害防治针对性,更好满足“预防为主,综合防治,绿色控害,减药增效”的要求,本文以江苏单季稻为例,利用历史气象资料和水稻生... 水稻始穗期是稻曲病、穗稻瘟、纹枯病等病害病菌侵入谷粒等器官造成稻米品质降低产量减少的关键期。为提高水稻病害防治针对性,更好满足“预防为主,综合防治,绿色控害,减药增效”的要求,本文以江苏单季稻为例,利用历史气象资料和水稻生育期观测资料,在分析水稻始穗期特征及其关键影响因子基础上,应用主成分分析法(PCA)、误差反向传播神经网络算法(BP)、随机森林算法(RF)研究水稻始穗期的预估方法。设置4组模拟方案分别建立水稻始穗期预测模型,以决定系数、均方根误差作为评判指标,对模型精度及其普适性进行分析评价。结果表明:苏北、苏中、苏南地区水稻始穗期的跨度分别在8月4−31日、8月9日−9月18日和8月16日−9月20日,各区平均标准差分别为4d、6d和5d;江苏各区影响水稻始穗期的关键因子基本一致,水稻始穗前3个生育期日序最为关键,播种−分蘖、分蘖−拔节、拔节−孕穗三个生育阶段的温度类因子重要性明显大于降水和日照类因子;与基于RF算法模型相比,基于BP算法的模型模拟精度更高,且对PCA处理后消除相关性的预测因子具有更好的“接纳性”,对江苏各区水稻始穗期模拟预测误差均在2d以内,预测提前量在10d左右,可为准确把握水稻病害防治关键期提供技术支撑。 展开更多
关键词 水稻 始穗期 主成分分析 随机森林算法 误差反向传播神经网络算法
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复杂小区二次供热管网仿真优化与泄漏检测 认领 引用
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作者 周守军 王耀龙 +3 位作者 刘晓康 刘书豪 董建敏 陈高强 《暖通空调》 2026年第8期102-110,共9页
为进一步提高集中供热管网泄漏故障检测效率,本文基于拟牛顿法中的BFGS(Broyden-Fletcher-Goldfarb-Shanno,布罗伊登-弗莱彻-戈德法布-香诺)算法和供热管网监测点实际压力运行数据优化供热管网仿真模型中的阻力特性系数,确保生成更精确... 为进一步提高集中供热管网泄漏故障检测效率,本文基于拟牛顿法中的BFGS(Broyden-Fletcher-Goldfarb-Shanno,布罗伊登-弗莱彻-戈德法布-香诺)算法和供热管网监测点实际压力运行数据优化供热管网仿真模型中的阻力特性系数,确保生成更精确的供热管网正常与泄漏工况的压力仿真数据。同时,结合主成分分析(PCA)方法和数据标准化方法,构建并训练用于管网泄漏检测的BPNN(back propagation neural network,反向传播神经网络)模型,开发了相应的泄漏检测系统。以山东某小区供热管网为对象,采用分级策略建立其复杂拓扑结构的四级水力工况仿真模型,研究实践结果表明:管网水力工况仿真模型经优化后,所有热用户的压力仿真数据相对误差均保持在5%以内;建立的供热管网泄漏检测模型,在检测距离小于50 m时,检测准确率达到93.8%,在检测距离为50~200 m时,检测准确率达到98.0%;开发的小区供热管网泄漏故障检测系统,经现场实测验证,系统反应灵敏,实际运行效果理想。 展开更多
关键词 供热管网 泄漏检测 仿真模型优化 BFGS算法 主成分分析(PCA) 反向传播神经网络(BPNN) 拓扑结构
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基于ANN算法的微纳米水气分散体系驱产油量预测方法 认领 引用
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作者 冯国庆 常海铃 +3 位作者 王苛宇 吴琳 伍家忠 王石头 《油气藏评价与开发》 CSCD 北大核心 2026年第2期414-422,共9页
微纳米水气分散体系驱(MNWDS)是1种新型的提高采收率技术,通过微纳米尺度的气水分散相注入,能够进入更小的孔隙空间,从而扩大了波及体积,有效提高了采收率。目前,该方法已在五里湾长6试验区开展矿场实验。在采用数值模拟方法预测微纳米... 微纳米水气分散体系驱(MNWDS)是1种新型的提高采收率技术,通过微纳米尺度的气水分散相注入,能够进入更小的孔隙空间,从而扩大了波及体积,有效提高了采收率。目前,该方法已在五里湾长6试验区开展矿场实验。在采用数值模拟方法预测微纳米水气分散体系驱的产油量时,需要考虑气泡尺寸、气液比、流体性质等多参数及复杂的气液相互作用,过程复杂且耗时长,无法快速模拟微纳米水气分散体系驱的产油量。为能够准确地预测注入微纳米水气分散体系驱后油井的产油量,该研究基于试验区实际生产数据和地质模型参数,运用人工神经网络(ANN)算法,建立了微纳米水气分散体系驱的产油量预测模型。该模型以试验区微纳米水气分散体系实施前油井的产油量、含水率、渗透率、注入微纳米水气分散体系量、水驱储量、孔隙度、有效厚度作为输入参数,以实施后12个月的产油量作为输出参数,建立了模型的训练样本集。通过对样本集进行K-Means(K-均值聚类算法)聚类分析,剔除了无效样本,最终形成了59个样本的训练集。在模型训练中,引入优化算法自动调整模型参数,显著提高了模型的测试集预测精度。基于此模型,对即将实施微纳米水气分散体系驱的21个井组进行了产油量预测,预测结果与数值模拟结果对比表明,二者的符合率高达95%,验证了该次模型的准确性。该模型为微纳米水气分散体系驱的产油量预测提供了1个新的途径。 展开更多
关键词 微纳米水气分散体系驱 机器学习 K-Means聚类分析 人工神经网络 莱文贝格-马夸特算法
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