Search Results (33)

With the increasing demand for reliable structural safety assessment in service, high-precision, non-contact, and long-term deformation monitoring has become increasingly urgent for large civil engineering structures. To address this need, this study proposes and validates a system-level non-contact monitoring framework that integrates an embedded vision-based deformation sensor with intelligent algorithms. Rather than treating individual techniques as isolated components, the proposed framework integrates high-precision optical imaging, subpixel localization, and intelligent image processing into a unified monitoring workflow. By continuously imaging and tracking targets on the structural surface, high-precision acquisition of two-dimensional dynamic displacements is achieved. To address issues such as image jitter, environmental disturbances, and camera-induced vibrations under long-distance imaging conditions, a hybrid algorithm based on signal processing and image correction is introduced to effectively compensate and filter the monitoring data, thereby significantly improving the stability and accuracy of deflection measurements. In engineering applications, a girder bridge and an integral open-box sluice structure were selected as monitoring objects, and field experiments were conducted over multiple periods under different working conditions. The results indicate that the proposed system can stably capture small structural displacements, achieving sub-millimeter measurement accuracy. The findings verify the feasibility and reliability of the proposed intelligent vision-based deformation monitoring technology in complex engineering environments, and provide a new technical approach for structural safety assessment and operational monitoring of infrastructure such as bridges and hydraulic structures. Full article

In recent years, as underwater vehicles continue to improve their noise reduction capabilities, sonar-based detection has faced significant challenges, and non-acoustic detection has become a research focus. Gravity gradient detection, owing to its excellent concealment and anti-interference capability, is regarded as an important non-acoustic means for underwater target detection. Based on the structural characteristics of an underwater vehicle, this paper establishes a homogeneous ellipsoidal hull (HEH) model composed of two similar rotating ellipsoids. This model assumes that the mass of an underwater vehicle is completely uniformly distributed over the outer hull. Analytical formulas for the gravity anomaly and gravity gradient anomaly generated by this model are derived, and their spatial distribution characteristics are analyzed. Furthermore, based on the HEH model, the feasibility underwater vehicle detection using the vertical gravity gradient component is analyzed. Results show that when the accuracy of the gravity gradiometer reaches ${10}^{-4}$ E, the detection distance for a large underwater vehicle with a displacement of 18,750 t can reach 570 m. Full article

Endocrine-Disrupting Chemicals (EDCs) disrupt thyroid hormone (TH) synthesis, transport, metabolism, and action, thereby perturbing systemic endocrine homeostasis. Transthyretin (TTR) is a key TH transport protein that regulates circulating hormone distribution and tissue availability, particularly during critical developmental windows. Chemical interference with TTR-binding may alter TH bioavailability and represent a transport-mediated molecular initiating event within thyroid-axis perturbation. Despite widespread exposure, many thyroidal EDCs remain unidentified, and their health effects are difficult to assess due to multiple simultaneous exposures. To support endocrine hazard identification and chemical prioritization within risk assessment frameworks, we developed machine learning-based QSAR models during the Tox24 challenge, using a dataset of 1512 chemicals to predict TTR-binding affinity. Of these, 67% were used for training, 13% for testing, and 20% for validation. Molecular descriptors were selected by first removing highly correlated features and then ranking the remaining descriptors using mutual information regression. The leverage approach was applied to define the models’ applicability domain (AD). Five machine learning algorithms, including gradient boosting regressor (GBR), Random Forest, Lasso Regression, Support Vector Machine (SVM), and regularized SVM models, were developed. The GBR model demonstrated the best overall performance. This model achieved an R² of 0.89 on the training set, 0.58 on the test set, and 0.55 on the validation set. The molecular descriptor analysis highlights hydrophobicity, steric effects, branching, connectivity, and ionization/electronic effects as the mechanistic basis for TTR disruption and stabilization, providing structural insight into features associated with thyroid hormone displacement. The AD analysis indicated that 97.5% of the test set and 96.0% of the validation set fell within the reliable descriptor space. Importantly, these predictions extend beyond model benchmarking by informing weight-of-evidence evaluations of thyroid-axis perturbation and supporting the prioritization of chemicals for targeted testing within non-animal new approach methodologies. Overall, this work highlights the application of in silico approaches for screening EDCs, supporting the prioritization and identification of potentially harmful chemicals. Full article

Bromodomain-containing protein 9 (BRD9) belongs to the non-canonical BAF chromatin remodeling complex and represents a relevant therapeutic target in pathologies featuring dysregulated epigenetic control. The absence of clinically validated inhibitors and the need for diversified chemical entities highlight the interest in identifying new scaffolds targeting this protein. In this study, Saturation Transfer Difference Nuclear Magnetic Resonance (STD NMR) was employed to assess its suitability for characterizing BRD9–ligand interactions within a fragment-based discovery framework. STD NMR conditions were first optimized using the known BRD9 ligand 1, verifying the presence of interaction signals. A pharmacophore-based virtual screening campaign was then performed using libraries of commercially available fragments, leading to the selection of a novel isatin derivative, i.e., compound 2, whose binding was demonstrated in AlphaScreen assays. STD NMR experiments provided epitope mapping consistent with the predicted binding mode, thus supporting the stability of the interaction in solution. Moreover, a competitive STD experiment demonstrated displacement of 2 by a reference ligand, confirming the binding within the canonical BRD9 pocket. Overall, this study establishes STD NMR as a reliable approach for probing BRD9–ligand interactions and for the identification and validation of BRD9-targeting scaffolds suitable for future structure-guided optimization. Full article

This study aims to demonstrate the feasibility of a hybrid measurement system that combines Laser Doppler Vibrometry (LDV) and conventional accelerometers for operational modal analysis (OMA) of civil engineering structures. The proposed approach addresses the limitations of traditional accelerometer-based systems, particularly for large-scale or inaccessible structures, by integrating non-contact LDV measurements with conventional sensor data. Experimental tests were conducted on a cantilever beam and a pedestrian laboratory footbridge to validate the hybrid system. The LDV was used to measure velocity at key points, while accelerometers provided complementary reference acceleration measurements. Reflective targets were employed to facilitate non-contact data collection, allowing for the subsequent reuse of these targets for repeated measurements. The velocity data from the LDV were differentiated to obtain acceleration and integrated to estimate displacement, enabling a direct combination with accelerometer data. ARTeMIS Modal software was utilized to process and analyze the collected data, successfully identifying the natural frequencies and vibration modes of both structures. The results demonstrate that the LDV–accelerometer hybrid system effectively captures the dynamic behavior of structures, offering a comprehensive solution for modal analysis without extensive sensor deployment. This approach provides significant advantages in scenarios where traditional methods are impractical, positioning the hybrid system as a promising tool for dynamic analysis and infrastructure monitoring of complex structures. Full article

Background: Temporomandibular disorders (TMDs) are the most common source of non-dental orofacial pain, with peak prevalence during adolescence and young adulthood—the same age group when orthodontic treatment is typically initiated. Although orthodontics is not a proven cause of TMD, pre-existing dysfunction may be aggravated during treatment, creating clinical and medico-legal risks. Objective: This paper proposes a structured diagnostic questionnaire and scoring framework for pre-orthodontic TMD assessment. The protocol aims to enhance the early recognition of high-risk patients, facilitate interdisciplinary communication, and lay a foundation for systematic validation. Methods: The framework was developed through synthesis of international diagnostic criteria (DC/TMD), a targeted narrative review of the literature, and expert clinical input. Diagnostic categories were selected based on prevalence, impact on orthodontic outcomes, and medico-legal significance. Weighted scoring stratifies patients into three pathways: (1) proceed with orthodontics without concern, (2) proceed with monitoring, or (3) defer orthodontics until TMD is managed. Results: The proposed questionnaire is designed to address inconsistencies in the literature by applying standardized diagnostic items and objective thresholds (e.g., jaw opening < 38 mm) and structured follow-up intervals. Case scenarios illustrate how risk stratification guides decision-making. The questionnaire includes intra-articular and pain-related TMD entities such as disk displacement, degenerative joint disease, myalgia, myofascial pain, arthralgia, headache, and trismus. The framework provides orthodontists with defensible baseline documentation while supporting safe and individualized patient care. Conclusions: Inconsistent diagnostic frameworks, malocclusion classifications, and outcome measures have fragmented the evidence base in orthodontics and TMD. The framework aims to provide orthodontists with structured baseline documentation that may support clinical decision-making and medico-legal risk management. Validation studies are required to establish psychometric reliability and international applicability. Full article

Photogrammetry offers a non-contact and efficient alternative for monitoring structural deformation and is particularly suited to large or complex surfaces such as masonry walls. This study proposes a spatio-temporal photogrammetric refinement framework that enhances the accuracy of three-dimensional (3D) deformation and strain analysis by integrating advanced filtering techniques into markerless image-based measurement workflows. A hybrid methodology was developed using natural image features extracted using the Speeded-Up Robust Features algorithm and refined through a three-stage filtering process: median absolute deviation filtering, Gaussian smoothing, and representative point selection. These techniques significantly mitigated the influence of noise and outliers on deformation and strain analysis. Comparative experiments using both manually placed targets and automatically extracted feature points on a full-scale masonry wall under destructive loading demonstrated that the proposed spatio-temporal filtering effectively improves the consistency of displacement and strain fields, achieving results comparable to traditional marker-based methods. Validation against laser rangefinder measurements confirmed sub-millimeter accuracy in displacement estimates. Additionally, strain analysis based on filtered data captured crack evolution patterns and spatial deformation behavior. Therefore, integrating photogrammetric 3D point tracking with spatio-temporal refinement provides a practical, accurate, and scalable approach to monitor structural deformation in civil engineering applications. Full article

In recent years, the development of computer vision technology has led to significant implementations of non-contact structural identification. This study investigates the performance offered by the Phase-Based Motion Magnification (PBMM) algorithm, which employs video acquisitions to estimate the displacements of target pixels and amplify vibrations occurring within a desired frequency band. Using low-cost acquisition setups, this technique can potentially replace the pointwise measurements provided by traditional contact sensors. The main novelty of this experimental research is the validation of PBMM-based experimental modal analyses on multi-storey frame structures with different stiffnesses, considering six structural layouts with different configurations of diagonal bracings. The PBMM results, both in terms of time series and identified modal parameters, are validated against benchmarks provided by an array of physically attached accelerometers. In addition, the influence of pixel intensity on estimates’ accuracy is investigated. Although the PBMM method shows limitations due to the low frame rates of the commercial cameras employed, along with an increase in the signal-to-noise ratio in correspondence of bracing nodes, this method turned out to be effective in modal identification for structures with modest variations in stiffness in terms of height. Moreover, the algorithm exhibits modest sensitivity to pixel intensity. An open access dataset containing video and sensor data recorded during the experiments, is available to support further research at the following https://doi.org/10.5281/zenodo.10412857. Full article

This paper presents research on active vibration control (A-V-C), which is being carried out to reduce structural vibration in the field of active vibration control and describes the most important method of implementation. Non-adaptive and adaptive systems feedback with adaptive algorithms are outlined. Electrodynamic shakers, used to excite an SDOF system to study its dynamic characteristics, are introduced. Signal analysis determines the response of a system under known excitation and presents it in a convenient form. The proposed method directly measures the payload displacement relative to the ground. We carry out a detailed investigation based on a realistic single-degree-of-freedom (SDOF), demonstrate the effectiveness of the proposed adaptive control law, estimate the control parameters, and show that the target dynamics of the isolator are attained. Full article

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments. Full article

To treat cardiovascular diseases (i.e., a major cause of mortality after cancers), endovascular-technique-based guidewire has been employed for intra-arterial navigation. To date, most commercially available guidewires (e.g., Terumo, Abbott, Cordis, etc.) are non-steerable, which is poorly suited to the human arterial system with numerous bifurcations and angulations. To reach a target artery, surgeons frequently opt for several tools (guidewires with different size integrated into angulated catheters) that might provoke arterial complications such as perforation or dissection. Steerable guidewires would, therefore, be of high interest to reduce surgical morbidity and mortality for patients as well as to simplify procedure for surgeons, thereby saving time and health costs. Regarding these reasons, our research involves the development of a smart steerable guidewire using electroactive polymer (EAP) capable of bending when subjected to an input voltage. The actuation performance of the developed device is assessed through the curvature behavior (i.e., the displacement and the angle of the bending) of a cantilever beam structure, consisting of single- or multi-stack EAP printed on a substrate. Compared to the single-stack architecture, the multi-stack gives rise to a significant increase in curvature, even when subjected to a moderate control voltage. As suggested by the design framework, the intrinsic physical properties (dielectric, electrical, and mechanical) of the EAP layer, together with the nature and thickness of all materials (EAP and substrate), do have strong effect on the bending response of the device. The analyses propose a comprehensive guideline to optimize the actuator performance based on an adequate selection of the relevant materials and geometric parameters. An analytical model together with a finite element model (FEM) are investigated to validate the experimental tests. Finally, the design guideline leads to an innovative structure (composed of a 10-stack active layer screen-printed on a thin substrate) capable of generating a large range of bending angle (up to 190°) under an acceptable input level of 550 V, which perfectly matches the standard of medical tools used for cardiovascular surgery. Full article

Structural health monitoring has gained increasing research interest, particularly due to the societal concerns tied to the aging of current civil structures and infrastructures. By managing datasets collected through a network of sensors deployed over monitored structures, (big) data analytics can be executed. Traditional inertial sensors, such as accelerometers or strain gauges, necessitate intricate cable arrangements and lead to high maintenance costs. Lately, there has been a growing interest in non-contact, vision-based approaches to tackle these aforementioned issues. Among these methods, digital image correlation (DIC) can furnish a representation of tracked displacements at various points of a structure, particularly if physically attached targets are employed. In this study, a video capturing the vibrations of a structure was analyzed, with a focus on specific points, such as structural nodes where damage could be initiated or whose responses could be impacted by the mentioned damage. Displacement time histories were acquired, and a blind source identification technique was adopted to delve into the data and assess structural health. The proposed methodology demonstrates its capacity to accurately extract the vibration frequencies and mode shapes of the structure, even when they change in time due to damage. Full article

Structural displacement monitoring is one of the major tasks of structural health monitoring and it is a significant challenge for research and engineering practices relating to large-scale civil structures. While computer vision-based structural monitoring has gained traction, current practices largely focus on laboratory experiments, small-scale structures, or close-range applications. This paper demonstrates its applications on three landmark long-span suspension bridges in Turkey: the First Bosphorus Bridge, the Second Bosphorus Bridge, and the Osman Gazi Bridge, among the longest landmark bridges in the world, with main spans of 1074 m, 1090 m, and 1550 m, respectively. The presented studies achieved non-contact displacement monitoring from a distance of 600 m, 755 m, and 1350 m for the respective bridges. The presented concepts, analysis, and results provide an overview of long-span bridge monitoring using computer vision-based monitoring. The results are assessed with conventional monitoring approaches and finite element analysis based on observed traffic conditions. Both displacements and dynamic frequencies align well with these conventional techniques and finite element analyses. This study also highlights the challenges of computer vision-based structural monitoring of long-span bridges and presents considerations such as the encountered adverse environmental factors, target and algorithm selection, and potential directions of future studies. Full article

Since all structures vibrate due to external loads, measuring and analyzing vibration data is a representative method of structural health monitoring. In this paper, we propose a non-contact cable estimation method using a vision sensor mounted on an unmanned aerial vehicle. A target cable among many cables can be identified through marker detection. In addition, the motion of the structure can be quickly captured using the extracted feature points. Although computer vision can be used to transform displacements of multiple axis, in this study, only the vertical displacement is considered to estimate tension. Finally, the cable tension can be estimated via the vibration method using the modal frequencies derived from the cable displacement. To verify the performance of the proposed method, lab-scale experiments were carried out and the results were compared with the conventional method based on the accelerometer. The proposed method showed a 3.54% error compared with the existing method and confirmed that the cable tension force can be estimated quickly at low cost. Full article

A refined design procedure for the seismic retrofit of warehouses or, more generally, single-storey RC frames bounded by “drift-sensitive” masonry infills and glazed curtain walls, is proposed in this paper by means of hysteretic braces. The calculation method is based on displacement-based design (DBD) procedures in which both the as-built frame and the dissipative braces are modelled through simple linear equivalent SDOF systems arranged in parallel. In this regard, with respect to the provisions of the Italian Building Code, two refinements are introduced: (1) the definition of two performance targets tailored to the protection of glazed curtain walls (among most expensive non-structural components) and to ensure an acceptable level of damage level for masonry infills; and (2) the adoption of a more accurate formulation for the estimation of the equivalent viscous damping developed both by the main frame and the dissipative braces. The refined design method is applied to a case-study building and the achievement of the performance targets is verified through NLTH analyses. Full article