21 results about "Moving load" patented technology

The present invention relates to a picking device (1) for picking at least one object (4), comprising a support structure, a movable load handling manipulator (2) arranged on the support structure, a handling element (3) arranged on the load handling manipulator (2) for handling the object (4), and a control unit (5) for controlling the load handling manipulator (2) and the handling element (3), wherein the handling element (3) has a vertically displaceable contact element (6) which can be applied to a front side (4-1) of an object (4) facing the handling element (3) and is configured to displace the front side (4-1) of the object (4) along a vertical object axis (8) in order to create a gap (9) below the front side (4-1) of the object (4), wherein the handling element (3) has two displaceable support elements (10, 10-1, 10-2).which are displaceable along a longitudinal axis (11) and along a transverse axis (12), wherein the displaceable support elements (10, 10-1, 10-2) can be attached to a bottom surface (4-2) of the object (4) through the gap (9) created below the front surface (4-1) of the object (4) in order to receive the object (4). The support elements (10, 10-1, 10-2) each have at least two spaced-apart fixing elements (13, 13-1, 13-2, 13-3, 13-4) that can be switched between a fixed state and a non-fixed state. The control (5) is configured to switch one fixing element (13, 13-1, 13-2, 13-3, 13-4) of the two support elements (10, 10-1, 10-2) into the fixed state during a rotation step and to cause a synchronous displacement of the two support elements (10, 10-1, 10-2) along the longitudinal axis (11) and transverse axis (12) in order to achieve a rotation of the object (4) about the vertical object axis (8).

The invention discloses a method for forecasting stress and fatigue life of an articulated joint of a marine emergency bridge under a moving load. A wave-bridge action numerical model is established in fluid software, and an external drainage basin and bridge motion numerical model is constructed according to computational fluid mechanics (fluid). Finite element calculation is carried out under the same model proportion, and the fluid and the finite element model have the same size and the same position in the coordinate system of the fluid and the finite element model. And constraining the finite element model, and further applying a moving load. And operating the fluid software, and mapping the pressure data of the fluid software to the finite element software. And feeding back the movement of the bridge and the deformation of the joint structure obtained in finite element analysis in the finite element software to the fluid solver through a grid mapping technology so as to update grid data in the fluid solver. By forecasting the deformation of the hinge joint of the marine emergency bridge in a multi-field coupling environment, more accurate reference data is provided for the design of the joint of the marine emergency bridge.

The application provides a modeling method of time-varying moving load in finite element method, comprising the following steps: establishing an initial mechanical finite element model of a structure to be calculated, and performing initial finite element mesh division; calculating the radius R of a contact spot between a force application surface and a force receiving surface by using a finite element method; selecting contact points within a width R range along a load moving path, and sequentially renumbering the contact points according to a load action time sequence to obtain finite element mesh nodes; establishing independent unit time load base functions at the finite element mesh nodes; calculating a total load F(t) of the structure at a time t; obtaining a coefficient item in a base function weighting coefficient vector at the time t by using a linear interpolation method; and decomposing the moving load F(t) to a plurality of mesh nodes near the load contact points based on an equation L(t) = f t ·W(t) to obtain a time-varying moving load loading result within a time [0, t1]. The method solves the moving load problem in the finite element environment.

This application relates to the field of building construction technology and discloses a vertical precast component positioning and installation system and method. The system includes a moving load-bearing mechanism, a component clamping and flipping mechanism, and a rebar correction and positioning mechanism. The component clamping and flipping mechanism clamps the vertical precast component and drives it from a horizontal transport state to an upright installation state. The rebar correction and positioning mechanism is used to position and correct the upper end of the exposed section of the pre-embedded rebar at the installation location. The rebar correction and positioning mechanism includes a correction bracket and multiple correction claws. In the retracted state, the correction bracket can be inserted into the installation area through the gap between adjacent pre-embedded rebars from one side. In the open state, the multiple correction claws respectively clamp the upper end of the corresponding exposed section of the pre-embedded rebar. After correction is completed, the component clamping and flipping mechanism drives the vertical precast component to descend, aligning the bottom precast hole with the pre-embedded rebar and fitting it in place for installation. This application can improve the hole alignment accuracy and construction efficiency of precast component installation.

This invention discloses a modular crossbeam structure for a gantry coordinate measuring machine, relating to the technical field of coordinate measuring equipment. It includes a modular triangular crossbeam, with a triangular socket at one end and a triangular plug at the other. The triangular plug and socket are fitted together. An inner shaft groove is formed at the center of the triangular plug, and an inner insertion shaft is rotatably mounted at the center of the triangular socket, fitting into the inner shaft groove. This invention employs a three-layer coaxial and concentric connection of the outer triangular profile, a middle locking pin layer, and a central inner insertion shaft, resulting in high joint stiffness, far exceeding that of traditional flange connections. The central inner insertion shaft penetrates the center of both crossbeam sections, forming a composite force transmission system of external triangular bending resistance and internal circular shaft torsion resistance, significantly improving bending, shear, and torsional stiffness. This completely eliminates bending deformation and step errors caused by the self-weight and moving loads of large-span crossbeams, ensuring long-term stability of the guide rail installation reference.

The invention belongs to the technical field of building robot detection, and particularly relates to a device and method for testing dumping and load of a wheeled robot on a slope, and the device comprises a counter-force beam support frame part which is disposed on a bottom platform and comprises a support oil cylinder used for preventing the robot from dumping left and right and being damaged; the counter-force beam moving load part is arranged at the top of the counter-force beam support frame part and is used for carrying out load test on the robot; the lane support frame part is arranged on the bottom platform and is used for carrying out a toppling test on the robot; and the wheel limiting liftable bottom plate part is arranged on the lane supporting frame part and is used for bearing a robot. According to the device and method for testing the toppling and load of the wheeled robot on the slope, the problem that existing detection equipment is difficult to adapt to wheeled engineering robots of different specifications to carry out slope range tests in which the wheeled engineering robots can safely pass under different load conditions can be solved.

This invention discloses a bridge damage identification method, system, electronic device, and storage medium based on multi-scale Gaussian filter residuals. The bridge damage identification method comprises the following steps: acquiring displacement response data of a simply supported beam of the bridge under test under moving load; performing principal component analysis (PCA) on the displacement response data and extracting key principal components using cumulative contribution rate (CCR); using a moving average filter (MAF) to filter out the high-frequency components in the dynamic components and obtaining low-frequency damage mode shapes; then using a Gaussian filter (GF) to simultaneously eliminate high-frequency dynamic components and most of the modal abrupt changes caused by damage, i.e., using a Gaussian function as a weighting kernel to reduce noise in the signal; and by analyzing the filtering residuals of the two filters, introducing an exponential function to amplify local peak values, effectively extracting damage features, thereby completing the structural damage identification and determining its specific location.

The invention relates to a transport system (100, 200, 300, 400, 500) for moving loads on an underlying surface, comprising: at least one rail travel path; and at least one transport vehicle (50) which runs on wheels (12, 12A) on the rail travel path, characterised in that: the rail travel path comprises a single rail (10, 312, 314, 412, 414, 512, 514) for receiving the load of the transport vehicle (50); the transport vehicle (50) comprises at least one front wheel (12) and at least one rear wheel (12A) which each rest on the rail (10, 312, 314, 412, 414, 512, 514) one behind the other in the travel direction by means of a running surface (14) having a concave cross-section; and the transport vehicle (50) further comprises support rollers (52A, 52B) which are arranged on both sides of the front and rear wheels (12, 12A) and run on the underlying surface on both sides of the rail (10, 312, 314, 412, 414, 512, 514).

"Portable and mobile construction crane under load" Portable and mobile construction crane under load (1), referred to as crane (1), comprising a chassis (2, 3) on which are mounted four wheels (230, 240, 330, 340), of which at least two are swiveling, a mast (4) the base of which is mounted swiveling on said chassis, said mast being swiveling, in a median longitudinal plane of the crane, around a transverse axis, a jib (6) fixed on the head of said mast, said jib being swiveling, relative to said head of said mast, around a transverse axis, and sliding, on the head of said mast, along a sliding axis, included in the median longitudinal plane of the crane, adjustable by swiveling said jib. The boom is suitable for receiving a gripping means (7) for a load to be moved (16), or one or more force transmission means (8) intended to support, at least in part, a load to be moved and / or a lifting means (9) for said load to be moved.The crane includes a jack (10), arranged to pivot said mast so as to carry a distal end of said boom cantilevered from said chassis, the base of which is mounted pivotally about a transverse axis distinct from the transverse axis about which the mast pivots, on the chassis; a head is mounted pivotally about a transverse axis on an upper quarter of the mast located on the side of said head of said mast, said jack being pivotable, relative to the mast and the chassis, in the median longitudinal plane of the crane. Figure for the abbreviation: Figure 1.

The invention relates to a crane system for handling a load (3, 4) and a method for controlling a load (3, 4) during a lifting operation. The method is used to control the orientation of the load (3, 4) during a lifting operation using a speed control method. A capstan (5) control system controls a capstan (5) arrangement, where the capstan (5) arrangement is arranged relative to the crane system. The method comprises the steps of: placing a load (3, 4) at a first load (3, 4) position and attaching a first tail cord (8) and a second tail cord (8, L2) to a first attachment point and a second attachment point on the load (3, 4); determining a predicted virtual axis of the position of the next load (3, 4) relative to the previous virtual axis; moving the load (3, 4) to the next load (3, 4) position relative to the predicted virtual axis while controlling each of the first and second winches (9) such that each of the first and second tail rope lengths (8) is individually adjusted relative to the predicted virtual axis; at the next load (3, 4) position, acquiring at least one piece of first tail line length (LI) information from the first measuring device and at least one piece of second tail line length (8) information from the second measuring device; analyzing the first tail rope length information and the second tail rope length (8) information relative to each other to determine an offset value for each of the first tail rope length and the second tail rope length (8), and comparing the offset values to a predicted virtual axis related to the current load (3, 4) position; an offset correction is calculated for the first tail line length and the second tail line length (8) compared to each offset value based on the predicted virtual axis. When the load (3, 4) moves towards another load (3, 4) position having another predicted virtual axis, the length of each respective tail rope (8) is adjusted relative to the predicted virtual axis such that the load (3, 4) is moved by pre-controlling the length of each respective tail rope (8).

This invention relates to the field of bridge testing and inspection technology, specifically disclosing a test method for damage identification of precast assembled beam bridges based on strain curves. The method includes: arranging multiple strain test points on the precast assembled beam bridge to be tested; applying a moving load and collecting strain data from the strain sections; extracting the strain data from each strain section and performing noise reduction processing on the strain data to obtain the strain influence lines for each strain section; determining the longitudinal damage location: determining the longitudinal damage location based on the changes in the intersection positions of strain influence lines at different spans. This invention uses strain influence lines at different spans of the longitudinal beams of the precast assembled beam bridge to correct errors caused by residual strain influence and reduce noise in the test data, reducing errors caused by equipment and environmental factors. Changes in the intersection points of the strain influence lines enable longitudinal damage identification of the precast assembled beam bridge. The test is short, has minimal impact on bridge traffic, reduces test costs, and improves economic efficiency.

The invention relates to the field of structural health monitoring, and discloses a multi-moving load identification method based on an influence line. Through calibration and comparison of bridge displacement response, high-precision inversion of an unknown moving load is realized. The method comprises the steps that firstly, a displacement sensor is arranged on a bridge, and the real-time displacement change of a structure in the vehicle passing process is obtained; then, a calibration vehicle with a known load passes through the bridge at a constant speed, displacement response of the calibration vehicle is collected, function fitting is carried out, and a calibration function with the load moving distance as an independent variable is obtained; on the basis, for a vehicle with an unknown load, the displacement response of the vehicle is also obtained, and functions D (x) and d (x) are established. And identifying the size of the unknown moving load by comparing the function relationship between the D (x) and the d (x). The method is clear in structure, simple to implement and high in anti-noise capability, can effectively improve the precision and stability of multi-load recognition, and has a good engineering application prospect.

The present application relates to a kind of bridge real-time monitoring methods based on double input self-encoding neural network, comprising the following steps: ①First, a small amount of dynamic vibration sensor is deployed in arbitrary position of bridge to collect vibration signal under the action of moving load;②Subsequently, signal is analyzed in spectrum, extract bridge structure fundamental frequency f1 as core parameter, based on the dynamic relationship of sampling frequency fs and fundamental frequency f1, according to the formula l≥2fs / f1 The rule is calculated to intercept moving window length;③Windowed signal is input into double-channel self-encoder neural network for training;④The network is used to realize structure state judgment after training, input the response data to be tested, output corresponding feature vector, and characteristic sensitive factor is obtained by calculation, whether the bridge structure has been damaged according to the change trend of characteristic sensitive factor is judged, and the real-time recognition of structure damage under the condition of unsupervised is realized.

The application discloses a bridge damage identification method under a moving load, comprising the following steps: 1, substituting bridge damage displacement data obtained through finite element simulation into a residual neural network to obtain a predicted damage reduction coefficient; 2, based on a Newmark-beta method, embedding available physical knowledge of a bridge structure into a feature learning process, and substituting the damage reduction coefficient predicted by the network into a physical equation to obtain predicted damage displacement data; 3, substituting the damage reduction coefficient and the displacement damage data obtained by solving into a loss function to obtain a loss value, and updating network parameters; and 4, predicting bridge damage by using the trained physical-guided residual neural network. The application has high damage identification precision under the condition of considering the uncertainty of vehicle weight and the uncertainty of bridge structure parameters, and can improve the accuracy of damage identification of the network when the measured data is less.

This application relates to a method for applying moving loads to a non-uniformly discretized finite element model, comprising: constructing a non-uniformly discretized finite element model, determining the node number and corresponding spatial coordinates of each finite element node, wherein the element length of the moving load is non-uniformly distributed; calculating the length of the element subjected to the moving load; and calculating the length of the moving load. i The time step j The spatial location of the moving load; determine the first i The time step j For each moving load element, the node number of the left node of the element at that time step is obtained and defined as a trajectory matrix. The distance between the moving load and the left node at that time step is calculated and defined as a distance matrix. This process is repeated to obtain the trajectory matrix and distance matrix of all moving loads within the total time steps. The equivalent nodal forces borne by the left and right nodes of the element when the moving load acts on it are calculated. This application dynamically tracks and precisely locates moving loads for accurate application throughout the entire time-history analysis.

The application provides a BVE quasi-static influence line identification method based on multi-observation joint estimation, comprising the following steps: S1, obtaining structural dynamic response data of a target bridge under the action of a moving load; S2, performing EMD-VMD multi-source observation construction and preprocessing on the structural dynamic response data to obtain multi-source observation data corresponding to the same quasi-static influence line; S3, establishing a quasi-static influence line parameterization expression model based on a shared node B spline, and performing joint fitting on the multi-source observation data; S4, performing adaptive adjustment on the joint fitting process of the shared node B spline based on an improved generalized cross-validation MGCV and a node encryption index; and S5, outputting the quasi-static influence line identification result of the target bridge. This method can reduce the influence of a single decomposition path, fixed smoothing parameters and fixed node configuration on the identification result, and improve the stability and peak area feature retention capability of the bridge quasi-static influence line identification.

The purpose of the present disclosure is to inhibit a decrease in the detection performance of a load sensor. A load detector (1) comprises a moving body (2), a support body (3), and a load sensor (4). The moving body (2) has a prescribed thickness. The support body (3) has a recess part (30) that accommodates the moving body (2) in a manner movable in a direction (D2) perpendicular to the thickness direction (D1) of the moving body (2). The load sensor (4) is interposed between an outer surface (2A) of the moving body (2) and an inner surface (3A) of the recess part (30), and detects a load that is applied to the moving body (2) in the perpendicular direction (D2). The load sensor (4) includes: a first structural part (4A) held on the moving body (2) side; and a second structural part (4B) held on the support body (3) side.

The application relates to the technical field of bridge test detection, and particularly discloses a prefabricated assembled beam bridge transverse damage rapid diagnosis method based on strain influence lines, which comprises the following steps: arranging two strain measuring points on a to-be-detected prefabricated assembled beam bridge; applying a moving load to the span direction of the prefabricated assembled beam bridge by adopting a quasi-static loading mode; extracting the strain of the two measuring points under the action of the moving load; and fitting and drawing a strain influence line; calculating the total envelope area S between the strain influence line of the bridge measuring point and the transverse coordinate axis 总 , then calculating the change amplitude of the total envelope area i ; and qualitatively diagnosing the prefabricated assembled beam bridge transverse damage according to the measuring point i . The prefabricated assembled beam bridge transverse damage rapid diagnosis method can comprehensively diagnose the prefabricated assembled beam bridge transverse damage, and improves the accuracy of the damage diagnosis result. The method has the advantages of short diagnosis time, uninterrupted traffic and the like, can rapidly diagnose, reduces the test cost, and improves the economy.

The application discloses a kind of plate combination structure vibration response analysis methods under mobile load, comprising the following steps: S1, obtains structure geometric parameter, structure material parameter and mobile load parameter;S2, based on the structure geometric parameter and structure material parameter, dynamic stiffness method is used to construct plate combination structure dynamic stiffness matrix;S3, based on the mobile load parameter, establishes mobile load semi-analytical model, utilizes trigonometric function integral transformation and fourier transformation and moves load from time domain-space domain to frequency domain-wave number domain, obtains frequency domain-wave number domain mobile load;S4, the plate combination structure dynamic stiffness matrix and frequency domain-wave number domain mobile load are solved, and the vibration response of plate combination structure frequency domain-wave number domain is obtained;S5, based on the vibration response of plate combination structure frequency domain-wave number domain, utilizes trigonometric function integral inverse transformation and fourier inverse transformation and obtains plate combination structure time domain-space domain vibration response.

The application discloses a bridge moving load identification method based on displacement response power spectrum, which comprises the following steps: (1) acquiring bridge displacement response power spectrum corresponding to different moving loads; (2) converting the bridge displacement response power spectrum into a color picture, taking the color picture and the corresponding moving load as the input and output of an AlexNet model respectively, and constructing a sample database; (3) training the mapping relationship between the color picture and the moving load by using the AlexNet model; and (4) identifying the moving load from the measured bridge displacement response power spectrum by using the trained mapping relationship. The application adopts a new bridge moving load identification method, considers the interference of random road excitation, directly identifies the moving load through the bridge displacement response power spectrum, is more in line with engineering practice, effectively solves the moving load identification under the influence of random road excitation, and provides support for efficient and accurate identification of the bridge moving load.

The application discloses a large piece of transport vehicle mixed traffic under bridge response machine learning evaluation method and system, and belongs to the technical field of civil engineering structure health monitoring and machine learning. The method comprises the following steps: establishing a grillage finite element model of a typical span bridge and extracting a load influence surface; adopting a moving load loading method to calculate a dynamic response history when mixed formation passes through the bridge; constructing a comprehensive database covering various bridge types, multiple large piece of transport vehicles and various design loads; establishing multiple machine learning prediction models with vehicle load distribution parameters and bridge key parameters as inputs and load effect amplification coefficients as outputs; and screening an optimal model to predict bridge load effect results under mixed traffic conditions. The application combines finite element simulation and machine learning, constructs training samples through offline simulation, predicts online, rapidly outputs evaluation results, and provides an efficient and accurate technical tool for large piece of transport vehicle approval and bridge safety operation and maintenance.