http://repositorio.lnec.pt:8080/jspui/handle/123456789/35 DE Sat, 04 Apr 2026 19:07:57 GMT 2026-04-04T19:07:57Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019001 Computer Vision System for Dimension Control in the Prefabrication of Concrete Panels Debus, P.; Valença, J. This paper presents a computer vision system for dimensional quality control of concrete panel formwork. The methodology combines corner detection with photogrammetric principles to analyse overhead images of formwork assemblies, comparing detected geometries with design specifications. Validation on synthetic images demonstrates high accuracy, with mean absolute errors below 1.12 mm for dimensional and 0.02° for orientation measurements. While application to real-world factory conditions revealed challenges in corner detection requiring future improvements, the established framework provides a foundation for automated quality control in concrete prefabrication, enabling early detection of assembly errors before concrete placement. Sun, 01 Jun 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019001 2025-06-01T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018999 Evaluation of Surface Quality in the Prefabrication of Concrete Panels using Computer Vision Debus, P.; Valença, J. The construction industry increasingly uses prefabrication for the significant advantages in cost-effectiveness, production speed, and environmental sustainability. Achieving high-quality building outcomes demands rigorous quality control of the individual building components. Concrete panel manufacturing presents unique challenges, particularly in surface quality assessment, as the material’s heterogeneity complicates standard evaluation metrics from industries like automotive manufacturing, where computer vision methods are well established. This research addresses these challenges with a robust computer vision-based methodology for surface quality assessment for prefabricated concrete panels, focusing on the detection of visible color differences using the ΔE metric. By recognizing the complex material properties and design requirements — including aesthetic aspects like color and texture — the proposed approach establishes more precise and adaptable evaluation techniques to enhance the overall quality and reliability of prefabricated concrete construction. Tue, 01 Apr 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018999 2025-04-01T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018996 Relevant channel selection in hyperspectral imaging to enhance crack segmentation in historic concrete buildings Valença, J.; Oliveira Santos, B. Recent years have been fruitful in the development of computer vision methods for a wide variety of applications. Despite the successful results achieved in the segmentation of cracks on concrete surfaces, poor results are still persisting during onsite application, mainly due to noise caused by biological colonization, which is present in most of historical heritage buildings. The authors have been working on this problematic and developed the SC-Crack method previously, however it still relies on the cumbersome task of acquiring sets of 17 channels images to compose an hyperspectral cube and still requires case-wise hyperparameter optimization. Consequently, it is important to define which spectral information mostly defines the success of the method, enabling to optimize both, the acquisition procedure and model processing. Following, a study aiming at the selection of the more informative channels was carried and the hyperparameter-free model is evaluated. In this scope, images of concrete specimens were acquired sequentially to compose a 17 channel hyperspectral image cube. These were sere compute allowing to define the most informative channels sets that are processed using the SC-Crack+ method, presented in this work. The reduced image cubes of cracking on clean concrete surfaces and on surfaces with biological colonization were processed and analyzed. Relevant and improved results were achieved for crack segmentation, following this SC-Crack+ model. This enables the possibility of mounting cameras with sensors and lenses particularly adapted for prone acquisition targeting only the most relevant hyperspectral information for crack segmentation and still using traditional feature engineering image processing methods. Sat, 01 Nov 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018996 2025-11-01T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018987 Development of an integrated software framework for enhanced hybrid simulation in structural testing Tekeste, G.G.; Correia, A.A.; Costa, A. Hybrid simulation integrates numerical and experimental techniques to analyze structural responses under static and dynamic loads. It physically tests components that are not fully characterized while modeling the rest of the structure numerically. Over the past two decades, hybrid testing platforms have become increasingly modular and versatile. This paper presents the development of a robust hybrid testing software framework at the National Laboratory for Civil Engineering (LNEC), Portugal, and evaluates the efficiency of its algorithms. The framework features a LabVIEW-based control and interface application that exchanges data with OpenSees via the OpenFresco middleware using a TCP/IP protocol. Designed for slow to real-time hybrid testing, it employs a predictor–corrector algorithm for motion control, enhanced by an adaptive time series (ATS)-based error tracking and delay compensation algorithm. Its modular design facilitates the integration of new simulation tools. The framework was first assessed through simulated hybrid tests, followed by validation via a hybrid test on a two-bay, one-story steel moment-resisting frame, where one exterior column was physically tested. The results emphasized the importance of the accurate system identification of the physical substructure and the precise calibration of the actuator control and delay compensation algorithms. Tue, 15 Apr 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018987 2025-04-15T00:00:00Z