3D Deformation Research Articles

3D displacement measurement using a single-camera and mesh deformation neural network

Computer vision-based methods for civil structure’s vibration displacement measurement have emerged as useful tools in the recent years. These methods offer several benefits including non-contact measurements, cost-effectiveness, and the ability to capture full-field displacement. Yet, there remain certain challenges. Measuring vibration displacement in 3D typically requires multiple cameras, adding complexity to camera configurations. Moreover, existing methods relied heavily on physical markers or natural key points. Placing physical markers on structures is often impractical, and natural key points are difficult to detect on structures with few distinct features or during rapid movements. Contrary to previous approaches, this paper presents a novel technique that uses a monocular camera for 3D displacement measurements. This technique obviates the need for physical markers or the reliance on natural key points, representing a significant advancement. Central to the method is a deep neural network designed to predict 3D mesh deformation directly from a single image input, combined with an initial 3D cube mesh input. A synthetic 3D dataset is generated to train the neural network. In the testing phase for real structures, advanced video segmentation method is employed to remove the background in order to enhance the prediction accuracy. The practical efficacy of this methodology is validated in a laboratory through a series of experimental tests on beam structures, demonstrating reliable results and application potentials.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.

Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks.

Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings. In this work, we present the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach for direct modeling of 3D cardiac mechanics of the biventricular anatomy between the extreme ends of the cardiac cycle. Its encoder-decoder architecture combines a low-dimensional latent space with recent advances in point cloud deep learning for effective multi-scale feature learning directly on flexible and memory-efficient point cloud representations of the cardiac anatomy. We first evaluate the PCD-Net's predictive capability for both cardiac contraction and relaxation on a large UK Biobank dataset of over 10,000 subjects and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. We then show the PCD-Net's ability to capture subpopulation-specific differences in 3D cardiac mechanics between normal and myocardial infarction (MI) subjects and visualize abnormal phenotypes between predicted normal 3D shapes and corresponding observed ones. Finally, we demonstrate that the PCD-Net's learned 3D deformation encodings outperform multiple clinical and machine learning benchmarks by 11% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.

Characteristic-Preserving Latent Space for Unpaired Cross-Domain Translation of 3D Point Clouds.

This article aims at unpaired shape-to-shape transformation for 3D point clouds, for instance, turning a chair to its table counterpart. Recent work for 3D shape transfer or deformation highly relies on paired inputs or specific correspondences. However, it is usually not feasible to assign precise correspondences or prepare paired data from two domains. A few methods start to study unpaired learning, but the characteristics of a source model may not be preserved after transformation. To overcome the difficulty of unpaired learning for transformation, we propose alternately training the autoencoder and translators to construct shape-aware latent space. This latent space based on novel loss functions enables our translators to transform 3D point clouds across domains and maintain the consistency of shape characteristics. We also crafted a test dataset to objectively evaluate the performance of point-cloud translation. The experiments demonstrate that our framework can construct high-quality models and retain more shape characteristics during cross-domain translation compared to the state-of-the-art methods. Moreover, we also present shape editing applications with our proposed latent space, including shape-style mixing and shape-type shifting, which do not require retraining a model.

Jellyfish-Inspired Visual and Sensory Bubbling Robots with Automatic 3D Morphable Films for Underwater Environmental Interactions.

Coelenterates, such as Atolla jellyfish, are capable of integrating color, communication, and motion in a sophisticated manner, thereby enabling them to function as intelligent biological systems that can adapt to the challenges of the underwater environment. Extensive efforts have been dedicated to exploiting underwater visual, sensory, actuating, or combined systems. However, current biomimetic soft systems are still limited by the lack of comprehensive, integrated functional skins that can automatically deform, dynamically sense, and further send color signals when diving into underwater conditions. Here, we propose the synthetic soft skins composed of assembled entangled carbon nanotube networks and fluorescent unit-embedded elastomers which can be applied in a suspended form to allow autonomic 3D deformation, real-time perception, and dynamic fluorescence color transformation. The capabilities of the sensory and color display thresholds were controlled through the entanglement density of carbon nanotubes and the suspended area. As a demonstration, the soft thin skin was integrated into an artificial jellyfish robot, enabling the realization of a closed-loop feedback system for dynamic sensory processing, signal processing, and further 3D morphing-induced fluorescent color change, demonstrating significant potentials in underwater visual display, danger warning, and environmental exploration.

Three-Dimensional Deformation Estimation from Multi-Temporal Real-Scene Models for Landslide Monitoring

This study proposes a three-dimensional (3D) deformation estimation framework based on the integration of shape and texture information for real-scene 3D model matching, effectively addressing the issue of deformation assessment in large-scale geological landslide areas. By extracting and merging the texture and shape features of matched points, correspondences between points in multi-temporal real-scene 3D models are established, resolving the difficulties faced by existing methods in achieving robust and high-precision 3D point matching over landslide areas. To ensure the complete coverage of the geological disaster area while enhancing computational efficiency during deformation estimation, a voxel-based thinning method to generate interest points is proposed. The effectiveness of the proposed method is validated through tests on a dataset from the Lijie north hill geological landslide area in Gansu Province, China. Experimental results demonstrate that the proposed method significantly outperforms existing classic and advanced methods in terms of matching accuracy metrics, and the accuracy of our deformation estimates is close to the actual measurements obtained from GNSS stations, with an average error of only 2.2 cm.

Individual and collective manipulation of multifunctional bimodal droplets in three dimensions.

Spatiotemporally controllable droplet manipulation is vital across numerous applications, particularly in miniature droplet robots known for their exceptional deformability. Despite notable advancements, current droplet control methods are predominantly limited to two-dimensional (2D) deformation and motion of an individual droplet, with minimal exploration of 3D manipulation and collective droplet behaviors. Here, we introduce a bimodal actuation strategy, merging magnetic and optical fields, for remote and programmable 3D guidance of individual ferrofluidic droplets and droplet collectives. The magnetic field induces a magnetic dipole force, prompting the formation of droplet collectives. Simultaneously, the optical field triggers isothermal changes in interfacial tension through Marangoni flows, enhancing buoyancy and facilitating 3D movements of individual and collective droplets. Moreover, these droplets can function autonomously as soft robots, capable of transporting objects. Alternatively, when combined with a hydrogel shell, they assemble into jellyfish-like robots, driven by sunlight. These findings present an efficient strategy for droplet manipulation, broadening the capabilities of droplet-based robotics.

Neural patient-specific 3D-2D registration in laparoscopic liver resection.

Augmented reality guidance in laparoscopic liver resection requires the registration of a preoperative 3D model to the intraoperative 2D image. However, 3D-2D liver registration poses challenges owing to the liver's flexibility, particularly in the limited visibility conditions of laparoscopy. Although promising, the current registration methods are computationally expensive and often necessitate manual initialisation. The first neural model predicting the registration (NM) is proposed, represented as 3D model deformation coefficients, from image landmarks. The strategy consists in training a patient-specific model based on synthetic data generated automatically from the patient's preoperative model. A liver shape modelling technique, which further reduces time complexity, is also proposed. The NM method was evaluated using the target registration error measure, showing an accuracy on par with existing methods, all based on numerical optimisation. Notably, NM runs much faster, offering the possibility of achieving real-time inference, a significant step ahead in this field. The proposed method represents the first neural method for 3D-2D liver registration. Preliminary experimental findings show comparable performance to existing methods, with superior computational efficiency. These results suggest a potential to deeply impact liver registration techniques.

3D deformation velocity field analysis and TEM method to detect the tectonic influence on the land subsidence in Zamora, Mexico

The city of Zamora is situated in a tectonic basin and, according to previous interferometric studies, has been experiencing sinking up to 13 cm/yr (2007-2011). Although the reported subsidence pattern (WNW-ESE) is similar to the orientation of the regional fault system, there is a lack of detailed studies to establish the connection between this phenomenon, the thickness of sediments, and the basement geometry. Therefore, this paper presents a 3D deformation velocity field analysis for the period of 2014–2021, using 130 images from the Sentinel-2 satellite to calculate the vertical and east-west components of the subsidence. Likewise, a campaign of 28 Transient Electromagnetic soundings was carried out around the city to determine the thickness of sediments and bedrock geometry. The results of the interferometric analysis reveal a WNW-ESE sinking pattern with the maximum vertical velocities observed near the Zamora Museum, amounting to 108 mm/yr (2014–2017) and 102 mm/yr (2018–2021). Meanwhile, the horizontal component has a heterogeneous behavior in both periods with rates reaching up to 1 cm/year. The Transient Electromagnetic survey results show the presence of two grabens, a horst, and a series of half-grabens. The thickness of sediments varies between 94 and 314 m, with the latter corresponding to the main graben located beneath the city. The axis of this structure and the largest sediment deposits are linked to the area of maximum sinking and the spatial pattern of subsidence.

Effects Of Wing Elasticity On Tip Vortex: 3D Deformation And 2D3C Flow Field Measurements

As aircraft embrace broader applications, the flexible wing presents a promising avenue to enhance aircraft performance across a wide range of flight conditions. Although the aerodynamic benefits of wing deformation are widely recognized, the present understanding of the tip vortex is relatively limited. This work experimentally examines the effects of wing deformation on the tip vortex of a simplified aircraft model with two kinds of elastic wings and a rigid wing. The 3D deformations and 2D3C flow fields are measured using a synchronous measurement system, which includes the high accuracy deformation measurement (HADM) method and the stereoscopic particle image velocimetry (SPIV). It is shown that increased wing elasticity leads to greater bending, with the wing tip moving along the streamwise, vertically upward, and spanwise inward directions. These deformations are closely linked to aerodynamic forces and influence the tip vortex positions. The more elastic wing, EW2, exhibits the highest vortex core position, more than 0.5 times the length of the wing root, while the other one, EW1, shows the strongest vortex in the lift-increased regime. In the early wakes, the tip vortex of the elastic wing is elongated along the spanwise direction, resulting in an asymmetric shape. As it convects downstream, the vortex core disperses via viscous diffusion, resulting in a relatively more circular shape with decreased vorticity.

Position control of a soft pneumatic actuator based on the pressure parameter feedback model (PPFM)

Abstract. Soft pneumatic actuators have been one of the cores of soft robotics research and play a key role in driving the development of soft robots. Due to its high degree of internal nonlinearity and unpredictable deformation caused by environmental influences, the control model established for soft robots is still a difficult problem in terms of improving accuracy. This paper proposes a new positional control method for soft pneumatic actuators that are suitable for independent 3D deformation at any position and are the core units of continuous robots. The pressure parameter feedback model (PPFM) of the airbag is obtained by adjusting the pressure input through a proportional valve, collecting the air pressure inside the airbag and obtaining the airbag expansion height. The pressure input signal is changed according to the PPFM of the airbag to control the position of the soft pneumatic actuator. A modular experimental platform is built to validate the PPFM-based control strategy, which is able to adjust the position of the end center point of the soft pneumatic actuator in space with the discussed characteristics. It is demonstrated that the theoretical model can significantly improve the stability and accuracy of the soft pneumatic actuator motion.

High-speed multi-camera videogrammetric measurement of full-field 3D motion and deformation in full-scale crash testing of typical civil aircraft

Crashworthiness has been and will continue to be the main concern in aircraft design. Crash tests are essential approaches to investigate the crashworthiness of civil aircraft. Videogrammetric methods have been employed in crash tests to overcome the disadvantages of laborious, time-consuming measurement data collection using contact sensors. However, previous methods using a single pair of cameras still face challenges in large-scale applications due to the inherent contradiction between measurement range and accuracy. In this paper, a videogrammetric method using four high-speed cameras is proposed to measure the full-field three-dimensional (3D) dynamic deformation for evaluating the impact response characteristics of the full-scale crash test aircraft. Increasing the number of high-speed cameras not only can enhance the resolution and then measurement accuracy of object, but also expands the measurement range significantly. To ensure the performance of digital image correlation (DIC) used for large-scale measurement range, a speckle pattern optimization design and fabrication method is presented to generate the random speckle with uniform spraying size and density. A global calibration method based on the high-precision total station is proposed to unify the data of different stereovision subsystems. The implementation of the proposed method is illustrated through a 6.0 m/s full-scale crash test using a civil aircraft approximately 24 m in length. The impact velocity, full-field displacement and strain of the fuselage structure are measured. The results show that the test data are complete and reliable. After the vertical crash at 5.71 m/s, the lower structure of the cabin floor is seriously deformed, and the upper structure of the fuselage in the central wing area is obviously deformed due to the inertia effect of the wing. The stiffness difference of the different fuselage segments results in significant differences in dynamic response. The higher the stiffness is, the smaller the deformation. This work may provide some valuable technical insights that could support the crashworthy design, verification, and certification of civil aircraft.

Assessment of new four variables 2D and quasi-3D higher order shear theories for bending analysis of different double curved FG shells

ABSRACT This study introduces a novel theory employing four unknowns to examine bending of a double-curvature functionally graded shells experiencing both uniform and sinusoidal pressure. This investigation utilizes 2D and quasi-3D higher order shear deformation theories, integrating a fresh model of a high-order displacement field. It focuses on exploring how geometric ratios, volume fraction and power law index influence the bending behavior, specifically examining their effects on the normal and shear stresses variation, along with their amplification-reduction impacts. The effectiveness of the developed model is evaluated by comparing its performance in simulating bending characteristics of plates, cylinders, spheres and ellipses with those presented in literature.

Urban Dark Fiber Distributed Acoustic Sensing for Bridge Monitoring

Distributed Acoustic Sensing (DAS) technology applied to telecommunication fiber optic networks offers new possibilities for Structural Health Monitoring (SHM). The dynamic responses of five bridges are extracted along a 24 km long optical fiber crossing the Lyon metropolitan area in France. From their characteristic seismic signatures, three physical parameters informing on the health of structures have been determined: vibration frequencies, damping and modal shapes. The fiber measurements are in agreement with velocimetric data serving as reference. The telecom optical fiber records the dynamic response of bridges in several directions and thus allows the reconstruction of 3D deformation modes using their orthogonality properties. Time tracking of frequencies, commonly used to assess structural integrity, shows that the average values of natural frequencies vary cyclically between day and night. The increase in frequencies during the night does not exceed 2% and probably reflects an overall stiffening of the structures due to the drop in temperature. The telecom fiber allows to obtain deformation and damping identity of structures, highlighting soil-structure coupling between the bridge and underlying soil. This study shows that it is possible to assess the spatial and temporal variability of bridge dynamic response from DAS data using existing fiber networks.

Correction of progressive collapsing foot deformity classes after isolated arthroscopic subtalar arthrodesis

IntroductionSubtalar osteoarthritis in the context of flatfoot (recently renamed Progressive Collapsing Foot Deformity (PCFD)) may be treated through subtalar joint (SJ) arthrodesis with anticipated consequences on three-dimensional bony configuration. This study investigates the correction of PCFD-related deformities achieved after Anterolateral Arthroscopic Subtalar Arthrodesis (ALAPSTA). MethodsIn this retrospective study, we evaluated pre- and post-operative (at 6 months) weight bearing computed tomography (WBCT) images of patients diagnosed with PCFD with a degenerated SJ (2 A according to PCFD classification) and/or peritalar subluxation (2D) with or without associated flexible midfoot and/or forefoot deformities (1B, 1 C and 1E) which underwent ALAPSTA as a standalone procedure between 2017 and 2020. Multiple measurements were used to assess and compare pre and post-operative PCFD classes. ResultsThirtythree PCFD (33 patients, median age 62) were included in the study. Preoperative medial facet subluxation was 28.3 % (IQR, 15.1 to 49.3 %). Overall PCFD 3D deformity improved with a reduction of the foot and ankle offset from 9.3 points (IQR, 7.8 to 12) to 4 (IQR, 0.9 to 7) (p < 0.001). Class A-hindfoot valgus (median tibiocalcaneal angle and median calcaneal moment arm improved by 9.4 degrees (p < 0.001) and 11 mm (p < 0.001), respectively), class B-midfoot abduction (median talonavicular coverage angle improved by 20.5 degrees, p < 0.001) and class C-forefoot varus (median sagittal talo-first metatarsal angle improved by 10.2 degrees (p < 0.001)) were significantly corrected after surgery. Class D was difficult to assess due to the fusion procedure. No patient had a pre-operative valgus deformity at the ankle (no class E), and no significant change of the talar tilt was observed (p = 0.12). ConclusionIn this series, ALAPSTA performed as a standalone procedure to treat patients diagnosed with PCFD with a degenerated subtalar joint and/or peritalar subluxation was effective not only at correcting hindfoot alignment but also flexible midfoot abduction and flexible forefoot varus. Level of evidenceLevel IV, case series

3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the iterative closest point (ICP) algorithm

Introduction: The conventional 3-D point cloud-based deformation analysis methods, such as the shortest distance (SD), cloud-to-cloud (C2C), and multiscale model-to-model cloud comparison (M3C2), essentially regard the closest distance between two periods of point cloud data as the deformation, rather than the true position of the same point in 3-D space before and after deformation.Methods: This paper proposes a method based on the ICP algorithm to calculate the differences between the chunked multi-period point clouds to recognizes the 3-D deformations.Results and discussion: The results show that the obtained results are very close to the GNSS data but with a much larger spatial monitoring range. The accuracy is higher than that of the SD method. Moreover, we analyze the statistical relationship between the point cloud block size and the deformation vector error and determine the optimal block size. The aim of this article is to optimize the deformation analysis method and improve its accuracy to provide techniques and ideas for the wider surface deformation monitoring research field. For instance, combining this method with data from contact methods constructs a 3D overall deformation model of the mountain, enabling real-time monitoring and early warning of debris flows.

Visco-elastohydrodynamic lubrication and wear model amended by deformation velocity

The deformation velocity caused by viscoelasticity is one of the drives of the Lubrication flow field and significantly influences the mixed lubrication and wear characteristics of journal bearings under dynamic loads. However, research considering the deformation velocity has yet to be reported. To address this research gap, we defined three-dimensional (3D) deformation velocities to amend the bearing surface velocity and proposed its calculation method based on the derivative function of the temporal association deformation equation and the finite element method. Therefore, a new mixed visco-elastohydrodynamic lubrication model amended by 3D deformation velocities was built and a nested solution method was proposed in which the sub-iterative process for deformation velocities was embedded into the oil film pressure iterative process. Furthermore, the lubrication model was coupled with the Archard model to predict wear. The viscoelastic parameter of the bearing was experimentally measured, and numerical simulations of lubrication and wear were performed for an ordinary bearing under a simple dynamic load and for the crankshaft main bearing of an internal combustion engine (ICE) under impact. The simulation results indicate that the deformation velocities reduce the extrusion effect and increase the contracting effect in the lubrication flow field. This enables the new model to identify the micro asperity contact, impact, and early wear features of bearings more accurately than the traditional mixed lubrication model. In addition, an increase in the ICE load intensifies the early wear of the bearing.

Novel steel connection for modular houses in indigenous communities: An experimental study

Remote Indigenous communities across Canada are facing housing shortages and the need for major repairs, which resulted from engineering challenges that obstructed the construction phases. Prefabricated steel moment-resisting frame modular houses are currently utilized as alternatives to typical conventical houses because of their technical advantages, such as better-quality control, lightweight structures, and ease of transportation to remote and rural regions. To explore the potential use of hollow structural sections (HSS) in panelized steel modular structures, this study presents a novel steel-bolted connection for connecting HSS beams to HSS columns using high-strength long bolts, indicating that the connections can be easily installed without requiring skilled workers and heavy machinery. Six specimens were fabricated and tested under monotonic load with a Digital Image correlation (DIC) camera to capture full-field 3D displacement and deformation until failure accurately. The impact of different geometric parameters such as bolt arrangement, extended plate thickness, number of bolts, and the existence of stiffener on the mechanical performance of the joint, stiffness, inelastic rotation, ductility, and failure modes were analyzed. Bolt arrangement with an aspect ratio of 1:1 was found to increase the connection capacity, initial stiffness, and ductility by 7%, 216%, and 26%, respectively. The yielding and rotation of the extended plate were eliminated using a stiffener. Increasing the number of bolts triggered the connection's ability to gain more capacity at earlier stages and reduced joint rotation by 60%.

3D large deformation modeling of the 2020 Gjerdrum quick clay landslide

A quick clay slide in Gjerdrum, Norway, occurred at 4 a.m. on 30th December 2020, killing ten people and destroying houses, roads, and other infrastructures. Approximately 1.35 million cubic meters of clay were released, a large volume liquefied, and debris was transported almost two kilometers downstream. An investigation following the slide determined that the slide was initialized in a 30-meter-high slope after 2-to-2,5-meter vertical erosion in a small creek running along the toe of the slope. After the initiation, the slide developed retrogressively in the order of 500 meters backward and sideways over a period of about 2 minutes. A conventional geotechnical slope stability analysis explains the initial slide. However, more advanced numerical tools are needed to simulate the retrogressive mechanism and the debris flow. The aim of the paper is to demonstrate a 3D Material Point Method model to capture some of the mechanisms involved from initiation until the debris comes to rest and how this method can be used to reproduce and study the processes involved in large deformation landslides.

InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China

Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas.