265 results about "Structural mechanics" patented technology

The invention discloses a steel-concrete hybrid girder bridge operation monitoring and static and dynamic analysis method based on multi-source data fusion, which comprises the steps of 1, structural response data acquisition, 2, multi-source data fusion processing, 3, building a plurality of operation state evaluation models based on parameters acquired by a sensor, and 4, carrying out static and dynamic analysis on the operation state evaluation models. 4, performing static analysis, dynamic analysis and static and dynamic collaborative analysis on the girder bridge based on the comprehensive feature set after multi-source data fusion, and 5, providing data support for the static and dynamic collaborative analysis by the operation state evaluation model through the comprehensive feature set, and meanwhile, evaluating the operation state of the girder bridge. Reversely supplementing the operation state evaluation model with the structural mechanical response characteristics excavated in the static analysis and dynamic analysis processes to form a data closed loop; the method has the advantages that through data sharing and feature interaction, evaluation result cross validation and multi-scale analysis linkage, cooperation of an operation state evaluation model and static and dynamic collaborative analysis is achieved, and a data closed-loop and multi-dimensional evaluation system is formed.

The invention discloses a monitoring and simulation mutual feedback correction concrete dam structure parameter intelligent identification method, and belongs to the technical field of dam safety monitoring, and the method comprises the steps: S1, building a finite element model, and generating a parameter set X through LHS; s2, calculating a water pressure component to obtain a data set Y, and normalizing a sample S = [X, Y]; s3, dividing the training set and the test set according to a ratio of 4: 1, and training a CBLA model; s4, the HST is used for separating the water pressure component, and ICPKO is combined with CBLA for optimization; and S5, iteratively terminating the output of the optimal parameter, and performing finite element verification. According to the concrete dam structure parameter intelligent identification method based on monitoring and simulation mutual feedback correction, the problem that a traditional algorithm is prone to being caught in local optimum is solved, the efficiency and precision of gravity dam structure mechanical parameter feedback are improved, the optimal combination of deformation moduli of a dam body and all partitions of a dam foundation meeting the actual situation is successfully inverted, and the method is suitable for popularization and application. And a new technical means is provided for structural response analysis of the gravity dam in the operation period.

The present application relates to a BIM-based hanging basket design method, computer device and computer-readable storage medium, wherein the design method comprises following steps: a parameter acquisition step; a hanging basket modularization step: decomposing structures of hanging baskets into a plurality of design modules; a selection step; a calculation step: calculating parameters of the design modules using a structural mechanics calculation method and / or a finite element calculation method; preliminary BIM model building step: building models of the design modules and a preliminary BIM model of the hanging basket; an optimization iteration step: converting the preliminary BIM model of the hanging basket into a finite element model for calculation, and feeding calculation results back to the preliminary BIM model for structural adjustment, repeatedly performing iterative calculations to obtain optimized hanging basket parameter data; final BIM model acquisition step: the BIM model of the hanging basket adjusted according to the optimized hanging basket parameter data is a final BIM model of the hanging basket. The design method enables autonomous calculation and design of the hanging basket with good versatility.

The invention relates to the technical field of crane stress analysis, in particular to a crane structure parameter stress analysis method and system. The method comprises the following steps: constructing an accurate three-dimensional structure simulation model by obtaining crane device data and operation logs, and carrying out crane operation analogue simulation based on the model; performing boom structure division on the simulation data, respectively obtaining simulation data of the truss boom type crane and the telescopic boom type crane, evaluating stress coupling increment of the truss boom type crane and telescopic boom type mechanical conduction response characteristics, and comprehensively detecting dynamic mechanical response of the crane; based on the dynamic response data, identifying the breakage risk of crane components, determining the chain type degradation condition of the components, and evaluating the operation stability gradient attenuation of the crane in combination with the three-dimensional structure model; carrying out structural mechanics optimization treatment according to the stability attenuation condition; through crane stress analysis, the crane can run more stably and efficiently.

The invention provides a bridge structure damage monitoring method and system based on multi-modal data fusion in the technical field of bridge structure damage monitoring. The method comprises the steps that S1, a server obtains a large amount of historical bridge monitoring data and emergency simulation data and inputs the historical bridge monitoring data and the emergency simulation data into a finite element model; s2, performing structural mechanical simulation on the finite element model to obtain bridge structure damage simulation data, and labeling historical bridge monitoring data through the bridge structure damage simulation data and the emergency simulation data to construct a data set; s3, training the bridge structure damage identification model through the data set; s4, the server collects real-time bridge monitoring data and inputs the real-time bridge monitoring data into the bridge structure damage recognition model, and a bridge structure damage recognition result is obtained; and S5, mapping the real-time bridge monitoring data and the bridge structure damage identification result to the BI model, and outputting a three-dimensional visualization report. The method has the advantages that the accuracy and the real-time performance of bridge structure damage monitoring are greatly improved.

The invention discloses a concrete arch dam global structural mechanical parameter zoning rapid inversion method, and relates to the technical field of dam operation safety monitoring and management, and the method comprises the steps: building a dam body and foundation three-dimensional finite element model through finite element software according to engineering design and monitoring data, and building a foundation three-dimensional finite element model according to damming material mechanical parameter information; partitioning the three-dimensional finite element model, analyzing the sensitivity of mechanical parameters of each region, determining sensitive mechanical parameters influencing the deformation of the concrete arch dam, and carrying out self-adaptive intelligent sampling on the sensitive mechanical parameters according to a sensitivity analysis result, and constructing a deep learning agent model reflecting a nonlinear relationship between the sensitive mechanical parameters of the dam and the deformation of each monitoring point, and carrying out deep learning inversion on the elastic modulus of the dam body and the deformation modulus of the bedrock. According to the invention, the method can efficiently and accurately invert and determine the structural mechanical parameters of the arch dam in the actual operation period, and provides a good basis for the safety analysis of the dam.

The invention discloses a multi-dimensional dynamic coupling water conservancy project toughness evaluation method. The method comprises the following steps: S1, data acquisition; s2, disaster chain dynamic simulation: training a GAN model by using the data collected in the step S1, the trained GAN model being used for generating virtual scenes of different disaster combinations, constructing a "structural mechanics-hydrodynamics-ecological model" coupling framework, simulating dynamic evolution after disasters occur, and generating multiple groups of disaster chain evolution processes; s3, calculating a comprehensive toughness index, and evaluating the engineering toughness of each group of disaster chain evolution process generated in the step S2; according to the method, a multi-dimensional dynamic coupling technology system is constructed, the precision and timeliness of toughness evaluation of the water conservancy project are improved, compared with a traditional static evaluation method, the technology realizes dynamic simulation of a disaster chain cascade process and quantitative analysis of multi-system interaction influence, and a more scientific solution is provided for safety management of the water conservancy project.

The invention discloses a system and method for health monitoring of a steel grid structure, and belongs to the technical field of structural mechanics testing, and the method comprises the steps: obtaining the position information of steel grid nodes, generating a node position distribution diagram through a self-positioning algorithm between wireless vibration sensors, recognizing a stress concentration region in the steel grid, and generating node key parameters. According to the node key parameters, the sampling frequency of the wireless vibration sensor is adjusted, and a dynamic sparse sampling instruction is generated; executing a dynamic sparse sampling instruction, and collecting steel net rack vibration data and current environment parameters; performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index; and when the comprehensive damage index exceeds a preset alarm threshold value, outputting an alarm signal. According to the method, the dynamic sparse sampling technology driven by the node key parameters and the multi-physics coupling analysis model are adopted, and low-energy-consumption and high-precision structural damage real-time monitoring and self-adaptive early warning can be achieved.

The invention provides a construction tunnel surrounding rock stability evaluation method based on digital twinning and a related device. The method comprises the following steps: constructing a tunnel digital twinning model of a tunnel construction section; geological data and real-time progress position data of a target mileage segment in the construction process are obtained; mapping the geological data to a digital twinborn model, and updating soil body unit parameters; according to the real-time progress position data, synchronously updating activation states of a soil body unit and a supporting structure unit in the model by adopting a state change method; and extracting a surrounding rock stability index based on the updated digital twinborn model, evaluating the stability of the current construction tunnel surrounding rock in combination with a safety threshold to obtain a construction tunnel surrounding rock stability evaluation result, and constructing a tunnel construction process dynamic mapping-oriented digital twinborn model by utilizing the description capability of a finite element method on structural mechanical behaviors. Geological conditions and construction progress information which are obtained in real time on site are used as input, and the construction period structure safety risk of the drilling and blasting method tunnel is evaluated and predicted.

The invention discloses a deep learning-based road and bridge construction measurement data processing method and system, and the method comprises the steps: extracting spatial topological features in point cloud data through a GCN graph convolutional network, extracting texture and structural features in image data through improved ResNet-50, extracting time sequence features in sensor data based on an LSTM long-short-term memory network, and carrying out the deep learning-based road and bridge construction measurement data processing. Introducing a multi-head self-attention mechanism to dynamically calculate weights of different modal features; constructing a VAE variational auto-encoder based on the fusion feature vector, judging an abnormal point through a reconstruction error, and performing secondary verification on the abnormal point in combination with a bridge structure mechanical model; and adopting a GAN generative adversarial network to generate alternative data of the abnormal point detection result, and selecting an optimal correction scheme through Bayesian optimization. And the data processing efficiency and the abnormal point detection accuracy are improved.

The invention belongs to the technical field of make-up torque calculation, and discloses a casing direct connection screwed joint make-up torque prediction method, through an empirical formula formed by fitting of a structural mechanical model and test data, the casing direct connection screwed joint make-up torque can be calculated directly by measuring steel pipe basic structure parameters, repeated tests are not needed, and the prediction accuracy is high. And the working efficiency is greatly improved. The prediction process only depends on basic structure parameters capable of being rapidly measured and is not limited by working conditions. A fitting formula comes from verification of a large amount of test data and theoretical models, and precision and universality are both considered; and multi-parameter factor comprehensive modeling is adopted, errors caused by a single factor are effectively eliminated, the accuracy of a prediction result is high, and the stability and reliability of torque prediction are improved. By reasonably predicting the screwing-on torque, the connection strength of the steel pipe is effectively guaranteed, and underground potential safety hazards caused by improper screwing or excessive screwing are avoided, so that the integrity of a shaft structure and the safety of construction operation are guaranteed.

The invention relates to the technical field of vehicle intelligent management, and discloses a vehicle state monitoring method and system based on deep learning, and the method comprises the steps: analyzing the physical dependence relation among vibration abnormality detection, loosening positioning and fatigue evaluation monitoring tasks based on structural mechanics constraints, and carrying out the monitoring of the vibration abnormality detection, the loosening positioning and the fatigue evaluation; generating a task dependence directed graph; inputting the multi-modal monitoring data matrix into a shared encoder of a multi-task learning network to generate a unified representation vector; respectively inputting the unified representation vector into a vibration anomaly detection decoder, a loose positioning decoder and a fatigue evaluation decoder, and outputting an initial prediction result of each task; and establishing a conditional probability model between task outputs based on the constraint relationship defined by the task dependent directed graph, and generating a consistency diagnosis result meeting physical constraints through maximum posteriori estimation. The technical problems of one-sided diagnosis results, mutual contradiction and low calculation efficiency are solved.

The invention discloses a U-shaped aqueduct prestress multi-beam cooperative tensioning control method and system. The method comprises the steps that S1, a quantitative derivation model is constructed, based on an aqueduct structural mechanical equilibrium equation, a prestress coupling effect item is introduced for calculation, a prestress coupling matrix is output, and an initial tensioning sequence is derived; the device is simple in structure, and an initial tensioning sequence is deduced by establishing a mechanical equilibrium equation and introducing a coupling effect item; meanwhile, stress / displacement deviation data collected by a monitoring system in real time are fed back to an edge calculation terminal to calculate a control strategy in real time, adjustment is executed by a tensioning device, and therefore a field data closed-loop optimization mechanism is constructed, the tensioning force and sequence can be dynamically corrected, the limitation of a static model is broken through, and the construction efficiency is improved. And a displacement constraint term is introduced as a punishment or control condition in the optimization process, so that deformation overrun of key parts of the structure is effectively prevented, and the precision, safety and intelligent level of prestress construction are remarkably improved.

The invention relates to the technical field of network public opinion monitoring, and discloses an emotion energy dynamic change-based public opinion inversion recognition method, which comprises the following steps of: constructing a weighted emotion potential energy field, and calculating effective emotion potential energy of nodes; and then calculating semantic field topological stress representing the viewpoint structure tearing degree and emotional potential energy field system entropy representing the energy distribution disorder degree, on this basis, constructing an entropy-stress two-phase state space and a dynamic hysteresis loop model, quantifying the social damping characteristics of the system, monitoring the system state in real time, and determining the state of the system. When the two-phase space yield judgment condition and the damping failure judgment condition are met at the same time, a public opinion inversion early warning signal and a risk probability index are output, the critical characteristics of public opinion system instability can be accurately recognized from the dimensions of structural mechanics and dynamics, and the false alarm rate of inversion early warning is effectively reduced.

The invention relates to the technical field of engineering monitoring, in particular to a bridge structure state monitoring method and system based on digital twinning, and the method comprises the steps: building a multi-source monitoring data matrix based on a data timestamp; establishing a geometric model, a material attribute model and a load model of a bridge structure based on the multi-source monitoring data matrix, and fusing to obtain a bridge digital twin initial model; and carrying out structural mechanical analysis based on the bridge digital twin initial model to obtain stress distribution data of each region of the bridge. Through real-time acquisition and analysis of multi-source monitoring data, the method can dynamically evaluate the structural state of the bridge and timely identify potential safety hazards, so that the safety of the bridge is improved; and through calibration of the initial model, the obtained digital twin precise model reflects the real state of the bridge, and a more accurate data basis is provided for subsequent structural analysis.

The invention provides a bridge health monitoring missing displacement reconstruction method based on a conditional diffusion model, which comprises the following steps: firstly, constructing a diffusion model taking multi-source physical sensing information as a guide condition, and inputting multi-source heterogeneous sensor information such as strain and acceleration as physical conditions; secondly, a residual module and a time coding mechanism are introduced in the reverse denoising process, and the time sequence feature and deep feature extraction capacity of the model is enhanced; and finally, gradually reconstructing a complete and physically reasonable displacement time history from missing displacement data containing noise by using the model, and realizing reliable reconstruction of bridge structure response. The method aims at stably and efficiently reconstructing a complete displacement time history which is accurate in numerical value and conforms to the structural mechanics principle from incomplete or completely missing displacement data, so that the integrity of SHM data is recovered, and a reliable data basis is provided for subsequent structural safety assessment and damage identification.

The invention discloses a steel tower stress analysis method based on twinborn simulation, which relates to the technical field of structural mechanics simulation and comprises the following steps: constructing a high-precision simulation layer and a rapid calculation layer according to structural parameters and material parameters of a steel tower; inputting a preset multi-field environment condition and structure state combination into the high-precision simulation layer to generate a stress response result, extracting stress mode features, and generating a multi-field stress mode feature library; steel tower structure response data and environment data are collected and preprocessed to form observation vectors, the observation vectors are compared and matched with the multi-field stress mode feature library, and target stress mode features and corresponding simulation configuration parameters are generated; and inputting the target stress mode characteristics, the simulation configuration parameters and the observation vectors into a rapid calculation layer to carry out twinborn simulation calculation, and generating a stress distribution data set and a response index set. According to the method, the multi-field stress mode feature library is constructed, so that the problems of high computing resource consumption and incapability of effectively utilizing historical data are solved, and the computing efficiency is improved.

The invention discloses a method for setting and controlling a buckle cable adjustment threshold value in large-span arch bridge hoisting construction, which comprises the following steps of: acquiring construction parameters of a current buckle section, and acquiring a corresponding cable adjustment target value; constructing a structural mechanics finite element model of the target buckle cable based on the construction parameters, and introducing a buckle cable multi-harness nonlinear stress mechanism into the model to calculate upper and lower limit thresholds of cable force in the tensioning process; determining an elevation adjustment threshold according to the target axis, the target elevation, the accumulated deviation and the pre-deformation compensation amount in combination with a geometric linear control model; and generating an environmental influence correction coefficient by utilizing construction environmental parameters, and dynamically correcting the cable force and the elevation threshold value to realize real-time updating of the threshold value in the hoisting process. According to the method, the problems of nonlinearity of multiple wire harnesses of the large-span arch bridge buckle cable, high linear sensitivity of the arch rib, remarkable environmental influence and the like are solved, high-precision cooperation of cable force control and linear control is realized, and the safety, controllability and construction efficiency of hoisting construction are improved.

The invention belongs to the technical field of composite material structural mechanics and computational mechanics, and particularly relates to a composite material fan blade composite crack propagation simulation method, program and equipment and a storage medium. According to the method, key rotation and a failure judgment criterion based on a key deflection angle are introduced into the conventional state near-field dynamic model, so that the crack propagation behavior of the composite material under the shear or mixed mode load can be more accurately captured and simulated. According to the flat plate mapping method, the improved PD model suitable for the flat plate is successfully expanded and applied to the fan blade airfoil section, the problem of complex geometric modeling is solved, and the flat plate mapping method is suitable for complex geometric structures. According to the method, a near-field dynamics method is adopted, crack paths do not need to be preset or grid reconstruction does not need to be carried out, and complex expansion behaviors such as spontaneous bifurcation and merging of cracks can be naturally simulated; the PD model can be used for meticulously describing the mechanical behaviors and crack evolution of each layer and interlayer in the laminated plate, including intra-layer cracking and interlayer layering.

The invention discloses an arch bridge tensioning method and system based on a physical constraint space-time diagram attention network, and the method comprises the steps: constructing a dynamic topological graph of an arch bridge cantilever construction structure, taking a structure initial state parameter, a control force system parameter and a real-time environment parameter as node input features, and taking a structure state response parameter as an output label, generating a training data set based on finite element model simulation; designing a deep learning network model combined with space diagram attention and time sequence causal convolution, and modeling a space-time dependency relationship; defining a mixed loss function of the data fidelity item and the physical residual loss item of the structural mechanical control equation; and training the model through a back propagation algorithm and deploying the model to a field decision control system. According to the method, through graph structure abstraction, spatio-temporal joint modeling and physical constraint embedding, multi-source monitoring data driven structure state online prediction and tension control strategy intelligent optimization are achieved.

The invention relates to the technical field of intelligent road engineering, in particular to a long-life pavement structure optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining pavement structure data, and constructing a structural mechanical model according to the pavement structure data to obtain the structural mechanical model; performing fatigue loss simulation according to the structural mechanical model to obtain fatigue loss data; carrying out rapid physical simulation according to the fatigue loss data to obtain loss simulation data; and performing physical prior life prediction according to the loss simulation data to obtain life prediction data. According to the method, through fracture entropy field modeling and crack density analysis, accurate quantification of spatial distribution and evolution trend of fatigue damage is realized. In combination with artificial intelligence-driven multi-time frame evolution simulation, the precision of pavement structure life prediction and the pertinence of design optimization can be significantly improved.

The invention relates to a mechanical analysis method for an extreme load of a concrete-filled steel tube arch bridge, which belongs to the field of engineering and structural mechanical analysis of the concrete-filled steel tube arch bridge, and comprises the following steps of: mapping a geometric model of the concrete-filled steel tube arch bridge to a high-dimensional super-bending space, and establishing a geometric model of an initial high-dimensional super-bending space; and in combination with finite element discretization and multi-physics field coupling analysis, a multi-physics field coupling analysis model is obtained. In combination with the damage degree evolution model, the damage degree is calculated in real time, and a model after real-time damage state calculation is formed; in combination with collected real-time monitoring data, predicting a damage evolution trend through an artificial intelligence model to obtain a model after damage evolution trend prediction; and constructing a multi-target repair optimization function, generating an optimal repair parameter combination of the concrete-filled steel tube bridge by adopting a particle swarm optimization algorithm, carrying out ultimate bearing capacity analysis and safety evaluation, and outputting a structural performance index. The method can prolong the service life and improve the performance of the concrete-filled steel tube arch bridge.

The invention relates to the technical field of building safety monitoring, in particular to a building construction safety monitoring system with automatic detection and danger early warning functions. Comprising a multi-dimensional data automatic acquisition unit; a dynamic risk analysis unit; a grading danger early warning unit; and an emergency linkage control unit. According to the invention, through the construction stage identification module, the weight dynamic adjustment module and the risk quantification and feature matching module, the current construction stage is determined based on the excavation depth of the foundation pit, the installation elevation of the steel structural member and the total height of the building; the weight ratio of each physical parameter is dynamically adjusted by combining the basic weight matrix and the mechanical influence factor matrix of the corresponding construction stage, then the comprehensive risk value is calculated through the single-parameter deviation degree and the deviation acceleration, the hidden danger type is matched, and the risk analysis key point can be adjusted according to the structural mechanical characteristics of different construction stages. The problems of lack of dynamic adaptation capability and limited risk assessment precision in the prior art are solved.

The invention discloses a full-underground multifunctional full-scale test loading device, and belongs to the technical field of large structural mechanical tests. The integrated counter-force mechanism comprises a counter-force wall which is arranged in the circumferential direction; the quick-release waterproof loading mechanism is arranged in the counter-force wall and is electrically connected with the control system; and the auxiliary system is arranged on the inner side of the quick-release waterproof loading mechanism. The device can be used for carrying out loading tests on large components with various section forms, such as shield tunnels, subsurface tunnels, pipe galleries, large-diameter pipelines and the like, and the utilization rate of the platform is remarkably improved. According to the integrated counter-force mechanism, the compressive strength, the shear strength and the impermeability of the counter-force well wall are greatly improved, the underwater test function is added, and meanwhile a foundation is provided for performance upgrading of a loading system in the later period.

The invention relates to a pipeline structure mechanics machine learning method based on a physical information neural network, and the method comprises the steps: building an equivalent variational differential equation according to pipeline structure mechanics, and carrying out the spatial discretization of a spatial domain related to the pipeline structure mechanics through a finite element method; aiming at a differential operator of the variable differential equation, based on a finite element method spatial discretization result, a local fitting method is adopted, and a convolution kernel field is obtained through conversion; coupling the convolution kernel field to a network architecture of a preset neural network, and collecting training data to train the neural network to obtain a trained physical information neural network; and based on the trained physical information neural network, performing calculation or simulation of pipeline structure mechanics.

The invention relates to the technical field of deep-sea mining vehicle lightweight, and provides a deep-sea mining vehicle lightweight design method based on multidisciplinary optimization, which fully considers the complex relation of mutual coupling among multiple disciplines, and realizes lightweight, low energy consumption and high reliability of a deep-sea mining vehicle. Comprising the following steps: S100, establishing a simplified model of the deep-sea mining vehicle; s200, in consideration of structural mechanics and material science, materials with smaller mass are selected to carry out material optimization on the deep-sea mining vehicle under the condition that the structural strength is met; s300, structural mechanics and fluid mechanics are considered, the component size and the water resistance coefficient are reduced under the condition that the structural strength is met, and structural optimization is conducted on the deep-sea mining vehicle; and S400, the reliability of the obtained lightweight deep-sea mining vehicle is ensured.

The invention provides a method for identifying early strength of concrete based on piezoelectric active sensing, which comprises the following steps of: monitoring sensing signals of all piezoelectric ceramic sensors belonging to a piezoelectric ceramic sensing array embedded in a concrete structure, identifying solidification characteristic information in the concrete structure through thermal infrared, and identifying the early strength of the concrete based on the solidification progress characteristic information. Effective sensing signals associated with the concrete in the early stage are screened, and it is guaranteed that the sensing signals accurately and truly reflect the structural mechanics condition of the concrete in the whole early stage; based on the spatial distribution information of the piezoelectric ceramic sensing array, the effective sensing signal is converted into three-dimensional pressure distribution information in the concrete structure; strength change characteristic information of the concrete structure is obtained through evolution analysis of three-dimensional pressure distribution information, so that a strength abnormal area in the concrete structure is identified, discrete strength data in the concrete structure is subjected to association processing, and overall strength evolution representation in the concrete structure is realized. And the identification credibility of the early strength of the concrete is improved.

The invention discloses a BIM-based large-space pipeline installation method and system, and relates to pipeline installation: performing three-dimensional gridding processing on BIM model data, generating a gridding space model, and constructing a pipeline installation space; calculating the load per unit length of the pipeline according to the self weight of the pipeline and the weight of a medium in the pipeline in the physical parameters; a dynamic planning method is adopted for optimization arrangement of the supports and hangers; constructing a state transition equation according to the load per unit length of the pipeline and the structural mechanics continuous beam model; according to the state transition equation, an optimal decision sequence is obtained through reverse recursion solution, backtracking is conducted from the tail end of the pipeline to the starting point, in each decision stage, the position of a supporting point enabling accumulated deflection of all follow-up pipe sections to be minimum is selected, and a globally optimal supporting and hanging bracket arrangement sequence is generated; according to the globally optimal arrangement sequence of the supports and the hangers and the load per unit length of the pipeline, the deflection value of each pipe section is calculated; according to the method, the problem of non-uniform deflection distribution caused by a traditional local optimization method is avoided.

The invention provides a dense pre-support structure thin shell mechanical modeling method, and relates to the technical field of tunnel engineering soft stratum pre-support structure mechanical modeling, and the method comprises the steps: calculating the equivalent stiffness of a pipe shed-jet grouting pile composite layer based on a Voigt theory; establishing a continuous thin shell model taking the control equation as a core; constructing a plastic / elastic disturbance partition in front of the tunnel face and a four-section type boundary condition of a supporting structure through a Pasternak foundation model; and solving the constructed continuous thin shell mechanical model through a finite difference method to obtain radial displacement, and completing mechanical modeling of the tunnel engineering soft stratum pre-supporting structure. The continuous thin shell model designed by the invention can provide high-reliability theoretical support for tunnel pre-support design.

The invention provides a composite material fan blade bending fracture forecasting method and device based on near-field dynamics and a storage medium, and belongs to the technical field of composite material structural mechanics and computational mechanics, and the method comprises the steps that a dangerous fan blade section under a preset load is determined; fan blade curves are integrated, an initial plane grid is created, polynomial fitting is carried out on discrete points, plane bending is carried out on transition area smoothing, meanwhile, error control is carried out at the large number position of the blade end, fan blade parameterized modeling is achieved, and the laying layer fiber direction is set; boundary conditions are applied, meshless discretization is carried out based on near-field dynamics, a material point motion equation is obtained, a near-field neighborhood is constructed, a family of each material point is searched, and a family member list is formed. The method is applied to bending deformation simulation of a large structure such as a fan blade section and damage simulation caused by deformation, modeling of the laminated plate is simplified by taking a single-layer material point as a laying layer, and natural crack propagation simulation is realized in combination with critical bending angle damage characteristics.