2111 results about "Pile" patented technology

The present invention relates to an automatic speed regulation and grouting regulation system for construction of a deep mixing pile in a reclamation area, the main steps comprising: first, configuring and installing a multi-modal sensing system (S101) to monitor key parameters, torque, energy consumption, grouting pressure and stratum permeability of a construction site in real time; constructing a data acquisition and fusion module (S102), integrating multi-sensor data, and forming real-time mapping of stratum-construction parameters; establishing a relation model of formation input and formation response through an extended Kalman filter (EKF) and predictive control (MPC) algorithm (S103), and intelligently evaluating formation compactness and grouting quality; on the basis of a feedback closed-loop regulation and control system (S104), the stirring speed, the grouting amount and the construction pressure are adaptively adjusted in real time, and precise control is achieved; through construction data storage and self-learning optimization (S105), the system records all construction data, and through a deep reinforcement learning optimization control algorithm, the long-term construction precision is improved.

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention relates to the technical field of real-time prediction of three-dimensional forms, in particular to a real-time dynamic prediction system and method for the three-dimensional form of a high-pressure jet grouting pile. The method comprises the following steps: carrying out stratum characteristic identification on a construction site to obtain stratum distribution data; key monitoring point positions are determined according to the stratum distribution data, and a multi-source sensor monitoring network is established; carrying out anti-interference optimization processing on the multi-source sensor monitoring network, and carrying out real-time acquisition on high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; and spatio-temporal evolution processing based on injection pressure, lifting speed and rotating speed is conducted on the real-time high-pressure jet grouting data, and parameter change characteristic data are generated. According to the method, the construction stratum distribution and the high-pressure jet grouting parameters are obtained and analyzed in real time, the parameter-stratum response relation model is established, grout diffusion and pile body forming dynamic prediction are conducted on different stratum characteristics in a targeted mode, and the precise construction capacity under the complex stratum condition is greatly improved.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention discloses a pile foundation integrity detection method which comprises the following steps: applying a variable amplitude sound wave excitation sequence to a pile foundation structure, collecting acoustic response signals under different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting material rheological characteristics; performing frequency dispersion analysis and hysteretic characteristic analysis on the nonlinear dynamic response data, extracting a stress memory dissipation coefficient and a nonlinear memory capacity index, and establishing a time-varying characteristic curve; according to an abnormal change interval and a memory capacity fading rate of the time-varying characteristic curve, determining a defect position and a defect type, and obtaining an evolution state parameter of the defect; and based on the historical change trend of the evolution state parameters, the material degradation rate and the structure damage development rate are calculated, the health state index of the pile foundation structure is predicted, and a full life cycle performance evolution curve is formed. Through stress memory activation and nonlinear dynamic response analysis, the detection method considering the rheological property of the material is established, and the performance evolution rule of the pile foundation in the whole life cycle can be accurately evaluated.

The invention provides a defect positioning accurate identification method considering the change of the section of a pile body, and the method comprises the steps: carrying out the simulation comparison of a constructed sound wave signal feature vector and a pre-constructed sound wave feature vector of a defect-free pile foundation, calculating the feature difference degree of the two, setting a difference degree threshold value, and if the feature difference degree exceeds the threshold value, carrying out the recognition of the defect-free pile foundation. If yes, preliminarily determining that the pile body has defects; on the basis of the adjusted sound wave detection, sound wave signals are collected again, signal processing and feature extraction are conducted through a self-adaptive path tracking algorithm, and the three-dimensional coordinates of the defect in the pile body are calculated according to the time delay and the propagation speed of reflected waves by combining the established multi-reflection model and an inversion sound wave propagation path; and according to the calculated three-dimensional coordinates of the defect reflection points, the influence degree of the defects on the bearing capacity of the pile foundation is calculated by combining the defect size estimated through the amplitude of reflected waves, the defect type recognized through waveform features and the strength parameter of a pile body material through a finite element method.

The invention relates to the technical field of pile foundation construction, in particular to a pile foundation pore-forming quality intelligent management and control method and system based on drilling machine working parameters, and the method comprises the following steps: constructing a data acquisition array, and obtaining key working parameters of a drilling machine and pile foundation pore-forming quality index data based on the data acquisition array; establishing a standard time sequence database; building a dynamic correlation model based on the standard time sequence database, and building a quality judgment standard of the pile foundation pore-forming based on the dynamic correlation model; dynamically predicting the pile foundation formed hole according to the dynamic correlation model to obtain the quality state of the pile foundation formed hole, and obtaining a quality deviation result of the pile foundation formed hole in combination with the quality judgment standard; and a regulation and control strategy of the drilling machine is constructed based on the quality deviation result, the key working parameters are adjusted according to the regulation and control strategy, and intelligent control over the quality of pile foundation hole forming is achieved. Intelligent control over the pile foundation hole forming quality is achieved through driving of the working parameters of the drilling machine.

The invention provides a method and system for predicting the uplift bearing capacity of a mud mixed pile. According to the method, modeling is carried out by integrating geomechanics and monitoring data. The method comprises the following steps: firstly, acquiring geometric parameters of a multi-layer heterogeneous soil layer interface, constructing a database through a mechanical test, and improving the reliability by combining gradient recognition and cooperative verification of pile deformation and load data; dividing longitudinal segmentation units, adjusting anti-slip parameters to generate a space mapping table, establishing a debonding dynamic model containing an interlayer retardation effect, simulating a debonding expansion process, and outputting load evolution data. And judging the maximum load of the critical state based on data convergence analysis, and taking the maximum load as a predicted value of the uplift bearing capacity. According to the technical scheme, through dynamic parameter mapping and debonding process simulation, accurate prediction of the uplift bearing capacity of the pile foundation in a complex soil layer is achieved, and the defect that a traditional model is insufficient in characterization of interlayer heterogeneity and dynamic debonding behaviors is overcome.

The invention relates to the technical field of civil engineering, and discloses a foundation pit slope protection pile concrete strength evaluation and intelligent reinforcement integrated method which comprises the following steps: S1, data acquisition and transmission: acquiring a slope protection pile structure state and environmental parameters in real time through a multi-source sensor network, the sensor network comprises a mechanical sensor, an environment sensor and a hydrological sensor which are deployed in a spatial distribution manner; s2, data preprocessing and feature extraction: performing space-time fusion processing, including noise suppression, space-time reference unification and multi-dimensional feature extraction, on the acquired multi-source sensing data, and generating a structured feature matrix; according to the technical scheme, the multi-source sensor network and the deep learning model are combined, the strength of the foundation pit slope protection pile concrete is monitored and evaluated in real time, the accurate and lossless strength prediction effect is achieved, the problems of destructive testing and precision limitation are avoided, and the test efficiency is improved. The defects of low precision and poor real-time performance under complex geological conditions in the prior art are overcome.

The invention relates to a charging pile fault diagnosis method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the fault recognition of a part of a target charging pile based on fault feature data, and obtaining a potential fault part; based on the component atlas, carrying out atlas construction on the potential fault component to obtain a potential fault component association atlas; analyzing an influence relationship among the potential fault components based on the potential fault component association graph to obtain a fault propagation path; calculating the fault probability of the potential fault component based on the fault propagation path through a preset Bayesian network model; and taking the potential fault component corresponding to the maximum fault probability as a fault source, analyzing the fault type of the fault source through a preset classification algorithm, and formulating a maintenance strategy of the target charging pile based on the fault type, thereby solving the problem that the fault diagnosis of the charging pile mainly depends on manual inspection and user feedback, so that the maintenance efficiency is improved. And the technical problems of slow response and poor accuracy exist.

The invention provides a prefabricated pipe pile construction total factor intelligent management method and system based on the digital twinborn technology, and relates to the technical field of construction management, a three-dimensional model is established through the BIM technology, pipe pile attributes are bound, construction data are collected in real time through the Internet of Things, and a dynamic digital twinborn body is formed. Through integration of an API interface and a project management platform, an AI algorithm is used for analyzing data, resource allocation and construction plans are dynamically adjusted, instructions are optimized and pushed to the site, construction efficiency and installation precision are improved, and the problems that traditional management is low in efficiency and large in error are solved.

The invention relates to the technical field of geotechnical engineering, and discloses a method, device and equipment for predicting the bearing capacity of an offshore wind power single pile and a medium, and the method comprises the steps that actual measurement data of different stratums where the single pile is located are obtained based on a pore pressure static sounding test; determining side friction resistance and unit pile end resistance of different stratums where the single pile is located based on measured data; the BP neural network model is trained based on preset single pile attribute data, the side friction resistance and the single pile end resistance, and a single pile bearing capacity prediction model is obtained; optimizing the single-pile bearing capacity prediction model based on a genetic algorithm to obtain a data processing type single-pile bearing capacity prediction model with gradually increased fitness; and solving the data processing type single-pile bearing capacity prediction model with gradually increased fitness to obtain an optimal solution of the single-pile bearing capacity. According to the method, the purpose that the accurate single-pile bearing capacity can be obtained only by conducting the pore pressure static sounding test subsequently is achieved, and the prediction accuracy is improved.

The present invention provides an intelligent construction control method, apparatus, and system for splitting jet grouting mixing piles, relating to geotechnical engineering. The method includes receiving real-time perception information from a construction information self-perception system, which includes drilling depth, drilling rig output power, drill bit torque, drill rod axial force, and pore water pressure of the drill bit. This information is input into a trained machine-learning model for stratum information, establishing a nonlinear implicit correspondence relationship between the perception information and construction stratum conditions, and outputting real-time stratum state information. The real-time stratum state information is then input into a trained self-matching machine-learning model for optimal construction parameters, establishing a nonlinear implicit correspondence relationship between the stratum state information and optimal construction parameters, and outputting current optimal construction parameters. This invention enables real-time intelligent inversion of stratum information and dynamic self-optimization control of construction parameters.

The invention relates to the technical field of digital twinning, and discloses a digital twinning modeling method, device and equipment, a storage medium and a product, and the method comprises the steps: building a static environment model through selecting a corresponding processing technology based on the characteristics of a fixed facility and the characteristics of an operation machine, obtaining the point cloud data of a material pile through a multi-source sensor and a two-dimensional laser radar, and obtaining the point cloud data of the material pile; obtaining a virtual material pile irregular triangulation network model through a point-by-point insertion algorithm based on the material pile point cloud data, extracting material pile surface data from the virtual material pile irregular triangulation network model, and constructing a dynamic scene model based on the material pile surface data according to the material pile boundary and the material pile surface data; and a digital twin bulk cargo model is obtained by integrating the static environment model and the dynamic scene model. According to the invention, based on the fixed facility features, the operation machinery features and the two-dimensional laser radar, the static environment model and the dynamic scene model are integrated, so that the bulk cargo modeling data precision and the model real-time mapping stability are improved.

The invention provides a desert photovoltaic support pile foundation uplift bearing capacity detection and construction parameter optimization method, and relates to the technical field of construction optimization, and the method comprises the following steps: dividing construction subareas according to soil parameter information of a desert target area, carrying out test pile detection in each construction subarea, and establishing a distribution rule of uplift bearing capacity of a pile-soil interface; the pile foundation diameter, the pile foundation burial depth and the pile foundation surface treatment mode serve as variables, a multi-objective optimization function including an uplift bearing capacity stability objective, an uplift bearing capacity redundancy objective and a construction efficiency objective is established, and the weight coefficient of the multi-objective optimization function is dynamically adjusted along with the environment temperature and sand bed migration; solving the multi-objective optimization function by adopting a particle swarm algorithm to obtain an optimal construction parameter combination of each construction subarea; and the optimal construction parameter combination is adopted for pile foundation construction, a pile foundation is selected for uplift bearing capacity rechecking, a rechecking result is fed back to the multi-objective optimization function, and dynamic fine adjustment is conducted on the construction parameters.

According to the pile foundation pile sinking monitoring method, device and equipment and the storage medium, the pile foundation pile sinking image is obtained through the collecting equipment arranged at the preset position, the pile foundation pile sinking image comprises the to-be-monitored pile foundation, and the to-be-monitored pile foundation is provided with a pile body scale; calculating the actual size of a unit pixel according to the pile body scale so as to determine a vertical pixel calibration value; according to a preset mark point and the vertical pixel calibration value, pile foundation pile sinking parameters are determined; and the pile sinking parameters of the pile foundation are input into a bearing capacity prediction model, and the predicted bearing capacity of the to-be-monitored pile foundation is obtained. The image acquisition device arranged at the preset position is used for acquiring the pile sinking image of the pile foundation in real time, pixel calibration is carried out in combination with the pile body scale, and key parameters in the pile sinking process of the pile foundation can be accurately calculated, so that a reliable basis is provided for prediction of the bearing capacity of the pile foundation, the monitoring precision and efficiency are greatly improved, and the cost is reduced. And errors possibly caused by manual measurement are avoided.

The invention provides an intelligent prediction method and device for the bearing capacity of a pile foundation, and the method comprises the steps: forming an in-situ test sample based on an engineering geology in-situ test; the optimal geological parameters of the karst geology are obtained through inversion of the proxy model; and geological parameters of the field test under multiple working conditions are obtained, and a pile foundation field test sample library is formed. Obtaining pile foundation bearing performance parameters of various karst geologies under a pile foundation reduced scale model experiment to form a pile foundation reduced scale model sample library; carrying out analogue simulation on the optimal geological parameters of each karst geology to form a pile foundation analogue simulation sample library; constructing a pile foundation bearing capacity training sample library by the three sample libraries; training the pile foundation bearing capacity prediction model; and geological parameters of a construction site are input into the pile foundation bearing capacity prediction model, and the pile foundation model of the actual construction site is optimized and adjusted based on the obtained geological parameters. According to the method, data of four research paradigms are fused, and a more comprehensive and accurate intelligent prediction method for the bearing capacity of the pile foundation is constructed.

The invention relates to a machine learning-based scouring damage bridge buckling load prediction method and system, and belongs to the technical field of machine learning and structural engineering. The prediction method comprises the steps of obtaining original data, preprocessing the original data, constructing a LightGBM machine learning model, and evaluating and verifying the constructed model by adopting multiple performance evaluation indexes. According to the method, key variables such as bridge structure parameters and foundation soil physical property indexes are comprehensively considered, a large sample database covering a large number of scouring combinations is constructed, and the critical buckling load of the structure is accurately calculated as an output target by using a pier-bearing platform-pile structure system buckling analysis method after scouring damage based on an energy method. A multivariable nonlinear mapping relation is established in combination with an ensemble learning algorithm, traditional complex calculation is replaced, and the method has good engineering adaptability and universality and can be suitable for rapid prediction of the buckling bearing capacity under different parameter bridges, different foundation forms and complex scouring conditions.

The invention discloses a charging pile intelligent distribution system based on dynamic load balancing and a charging pile, and relates to the technical field of charging piles, a distribution module is used for receiving an operation feature group of each charging pile at a first response moment, generating a feature weight set, and simulating importance judgment logic in the feature weight set according to a feature priority, dynamic load distribution of the charging piles is realized; the process module is used for receiving the data of each source in real time in the dynamic load distribution process, performing multi-source analysis, checking the fault burstiness of the charging pile, analyzing the communication interruption, predicting the charging demand performance and the power supply performance based on the data of each source at a second response moment, and constructing a comprehensive risk index in combination with an evidence theory; and the updating module is used for generating a judgment signal according to the comprehensive risk index value, and the judgment signal comprises an updating instruction and an iteration result.

The invention relates to the field of ocean engineering construction monitoring, and discloses a pile body penetration identification method based on multi-modal perception and dynamic coupling modeling, which comprises the following steps: collecting pile body motion data by deploying vision, inertia and laser ranging sensor networks, constructing a pile body-foundation dynamic model in combination with finite element discretization, and constructing a pile body penetration identification model; separating vibration interference and real displacement by using extended Kalman filtering; the YOLOv8 model is improved to realize pile body contour tracking in a low-illumination environment, and multi-source data is fused to predict a pile body motion state; the high-precision penetration is calculated through vibration compensation correction, weighted fusion and dynamic threshold judgment, and grading alarm and construction control instructions are triggered. According to the invention, the problems of low multi-sensor data fusion precision and weak anti-interference capability in a complex marine environment are solved, real-time monitoring and closed-loop feedback of the penetration of the pile body are realized, and reliable technical support is provided for piling construction.

The invention belongs to the technical field of supporting structure performance evaluation, and particularly discloses and provides an NT-CEP pile wall anchor foundation pit supporting structure anti-overturning evaluation method which comprises the steps that in the whole construction period, the construction process is divided into a plurality of continuous load steps based on excavation and supporting procedures; under each load step, pile body strain, arc-shaped wall stress, anchor rod axial force and deep soil displacement are synchronously collected, and a multi-source response data set is constructed; constructing an NT-CEP pile-arc wall-anchor rod coupling model based on the design drawing; carrying out inversion analysis through the model, and solving the load sharing proportion and the overall anti-overturning safety coefficient of each component under each load step; and continuously identifying the abnormal component based on the load sharing proportion, and dynamically analyzing and outputting an anti-overturning capability evaluation index based on the change trend of the safety coefficient. According to the method, dynamic refined evaluation of the construction process is realized, and the authenticity and perspectiveness of an evaluation result are remarkably improved.

The invention provides a charging pile operation and maintenance management method and system. The method comprises the following steps: acquiring an operation state parameter and an environment parameter of the charging pile; inputting the operation state parameters and the environment parameters into a multi-task prediction model to obtain the residual life and confidence interval of each key component of the charging pile; wherein the multi-task prediction model comprises a shared feature extraction module and a plurality of life prediction branches, and each life prediction branch corresponds to one key component; and determining a fault risk level of the charging pile based on the residual life and the confidence interval of each key component, and performing operation and maintenance management on the charging pile based on the fault risk level. Accurate and reliable data basis can be provided for operation and maintenance management of the charging pile, conversion of operation and maintenance of the charging pile from passive response to active prevention can be achieved, and remarkable breakthrough in the aspects of prediction precision, cost efficiency and system reliability is achieved.

According to the offshore wind turbine single pile rigidity numerical simulation method considering long-term circulation and scouring, the accuracy and the calculation convenience are both considered in model building through the linear elastic steel pipe pile model and the Mohr-Coulomb soil body model. By calculating the horizontal ultimate bearing capacity of the single pile, combining the ultimate analysis method and the p-y curve calculation method, the influence of different scouring depths on the fan is considered, the horizontal load bearing capacity and inherent frequency evolution of the single pile are comprehensively measured, and a basis is provided for design and safety evaluation. A sandy soil rigidity attenuation model is established for secondary development of Abaqus and verified, and the rigidity change of sandy soil under long-term cyclic loading is considered, so that the simulation precision is improved. And the numerical model is tested and verified to ensure the reliability. And the accumulated lateral displacement of the steel pipe pile can be accurately predicted, the influence rule of cyclic load times, soil elasticity modulus and embedding depth factors on displacement development is disclosed, wind power engineering design optimization is assisted, and economical efficiency and safety are improved.

The invention discloses a karst-rich geological pile foundation construction quality detection method, and belongs to the technical field of engineering construction quality detection. The method comprises the following steps before construction: constructing a digital three-dimensional model of a site and a geologic structure; creating an ideal construction model based on the constructed digital three-dimensional model; a special tracing material is used in the construction process and is used for tracing the flowing state and the distribution condition of the pouring material in real time; a pressure sensor, a displacement sensor, a tilt angle sensor and a detector specially used for detecting a tracing material are uniformly arranged along a construction area to form an omnibearing monitoring network; in the construction process, monitoring data are analyzed in real time to recognize an abnormal mode, and construction parameters are dynamically adjusted according to the abnormal mode; the construction process is visually presented, and potential problems are predicted; the construction quality is comprehensively evaluated by utilizing a tracing material in combination with a multi-source detection technology, so that the existing defect problem is accurately positioned.

The invention discloses a charging pile direct current power distribution method based on chaos topology, and the method comprises the steps: building a charging pile power initial chaos topology distribution network based on a matrix topology network according to user demands; expanding the dimension of the charging pile power initial chaotic topological distribution network to a high-dimensional curved surface through a Gaussian kernel function to obtain a charging pile power chaotic topological distribution network with a plurality of charging nodes; on the basis of the charging pile power chaos topology distribution network, the required power of each charging gun and a power distribution strategy, controlling on and off of a contactor switch unit so as to control guiding of power output by a power conversion unit; therefore, efficient power flexible scheduling can be realized, the degree of freedom of all nodes in the power topology can be customized at will, and it is ensured that each node can reach the efficiency expected by a user.

The invention provides a single pile bearing capacity prediction method based on an XGBoost machine learning algorithm, comprising the following steps: (1) acquiring and preprocessing test data including soil layer input parameters, pile parameters and construction parameters, and dividing the preprocessed test data into a test set and a training set; (2) constructing a machine learning model in a pile foundation design stage according to the soil layer input parameters and the pile parameters, constructing a machine learning model in a pile foundation construction stage according to the soil layer input parameters, the pile parameters and the construction parameters, and respectively optimizing the two machine learning models by adopting a Bayesian optimization algorithm; and (3) the two optimized machine learning models are used for calculating the single-pile bearing capacity in the pile foundation design stage and the single-pile bearing capacity in the construction stage according to needs. According to the method, influence factors of all stages are comprehensively considered, algorithm learning is carried out on the influence factors, the bearing capacity of the reinforced concrete prefabricated pipe pile can be rapidly and effectively predicted, and the method can be used for optimizing the pile length design, reducing the pile material cost and improving the construction quality.

The invention discloses a method and a system for estimating the pile length of an engineering pile. The method comprises the following steps: acquiring a multi-source detection signal reflecting the striking response of a pile hammer and stratum prior information; on the basis of stratum prior information, carrying out physically-driven adaptive analysis on sound wave components in the multi-source detection signals, and extracting preliminary sound wave reflection features; performing correlation verification on the preliminary sound wave reflection characteristics by using vibration components in the multi-source detection signals to generate corrected reflection characteristics; and the signal quality of the corrected reflection characteristics is evaluated, an intelligent hammering control instruction is generated according to the signal quality so as to execute closed-loop enhanced signal excitation, finally, multi-modal information before and after enhancement is fused, and a final pile length estimated value is obtained through calculation. Through sound and vibration coupling verification and intelligent active excitation, the accuracy, reliability and detection depth of pile length estimation under the conditions of strong noise, complex stratum and large depth are improved.

The invention provides a computer-aided slide-resistant pile and anchor cable build-in depth design method, and relates to the technical field of slide-resistant pile and anchor cable design, and the method comprises the following steps: collecting drilling data of an engineering site, and establishing a three-dimensional geologic model containing spatial distribution of each soil layer; determining a feasible range of design parameters; according to the soil layer space distribution and parameters, a slope stability analysis model considering the supporting effect of the slide-resistant piles and the anchor cables is established and used for calculating the slope stability safety coefficient under the given design parameters; and taking the minimum construction cost as a target function, determining an optimization constraint condition according to the slope stability analysis model, carrying out iterative optimization on the design parameters in a feasible range by adopting an optimization algorithm until an optimization convergence condition is met, and outputting an optimal design parameter. The problems that a traditional design method excessively depends on experience, multiple influence factors are difficult to consider, and the calculation efficiency is low can be solved, and the design level of the built-in depth of the slide-resistant pile and the anchor cable is improved.