National Laboratory for Civil Engineering

Transverse stiffeners provide lateral support to slender webs along their span, enhancing the shear buckling resistance of steel plate girders. Moreover, intermediate transverse stiffeners are particularly crucial, because they anchor the forces generated by tension field action, mitigate out-of-web buckling, and support the edges of the web panel. The standard EN 1993-1-5 outlines three requirements for the design of intermediate transverse stiffeners: (1) strength verification, (2) a minimum elastic bending rigidity, and (3) a torsional rigidity check. However, research has demonstrated that applying the strength verification criterion often results in overly conservative designs due to assumed internal forces significantly exceeding those observed in numerical simulations and experimental studies. Consequently, US design standards have omitted this verification, focusing solely on the minimum bending rigidity requirement. A numerical study of 13,500 plate girders with diverse geometries and stiffener configurations was conducted to evaluate the minimum bending rigidity required for intermediate transverse stiffeners to ensure the ultimate shear resistance of steel plate girders. The analysis considered the effects of steel grade, web slenderness and aspect ratio, flange-to-web area ratio, and the presence of longitudinal stiffeners. The results indicate that the required minimum bending rigidity is particularly high for plate girders with slender webs, low aspect ratios, and high flange-to-web area ratios.

Abstract:This study develops an artificial neural network (ANN)-based ground motion model (GMM) for the Azores Plateau (Portugal) using a dataset generated through stochastic finite-fault simulations. The simulations are performed for both onshore and offshore rock-site scenarios, employing a dynamic corner-frequency algorithm. Randomized source and path parameters are incorporated to capture the aleatory variability of regional seismicity. The simulated ground motions are validated through a comprehensive statistical framework, confirming that the implemented randomization reproduces realistic variance and inter-period correlations observed in recorded data. The ANN-based GMM is trained using the simulated database to predict spectral acceleration across a wide range of magnitudes and source-to-site distances. The developed model and accompanying dataset together provide a reliable foundation for seismic hazard and risk assessments in the Azores Plateau region.Keywords: Artificial neural network (ANN); Ground motion model (GMM); Stochastic finite-fault simulation; Onshore and offshore scenarios; Spectral acceleration prediction; Azores Plateau (Portugal).Acknowledgments:This work is financed by national funds through FCT – Foundation for Science and Technology, under grant agreement [2023.08982.CEECIND/CP2841/CT0033] attributed to the first author (https://doi.org/10.54499/2023.08982.CEECIND/CP2841/CT0033). This work was also supported by FCT/ Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the references UID/4029/2025 (https://doi.org/10.54499/UID/04029/2025) and UID/PRR/04029/2025 (https://doi.org/10.54499/UID/PRR/04029/2025), and under the Associate Laboratory Advanced Production and Intelligent Systems (ARISE) under reference LA/P/0112/2020. This work is partly financed by national funds through FCT (Foundation for Science and Technology), under grant agreement [UI/BD/153379/2022] attributed to the second author. This work is partly financed by national funds through FCT – Foundation for Science and Technology, under grant agreement [2023.01101.BD] attributed to the third author.

Abstract The accuracy of flood hazard mapping in dam-break modeling depends strongly on the quality of the Digital Elevation Model (DEM). This study evaluates six open-access global DEMs (SRTM, ALOS PALSAR, AW3D30, COPERNICUS, FABDEM, and ANADEM) in dam-break simulations for two Brazilian dams (Negreiros and Copiti), using HEC-RAS 2D and high-resolution LiDAR as reference. Four aspects were analyzed: (1) elevation accuracy; (2) flood wave propagation, including peak discharge and time-to-peak; (3) inundation extent, with over- and underestimation indices; and (4) hydrodynamic risk. Results show that AW3D30 consistently produced flood propagation, extent, and risk estimates that are closest to those from LiDAR. In contrast, SRTM and PALSAR systematically underestimated inundation areas, whereas FABDEM and ANADEM tended to overestimate, resulting in conservative risk scenarios. COPERNICUS and FABDEM achieved the highest altimetric accuracy but only moderate hydraulic performance. Across case studies, dam size, geomorphology, and distance downstream significantly affected DEM reliability, with mismatches becoming more pronounced the further downstream. The findings highlight the influence of DEM choice on flood modeling and guide dam-break risk assessment in semi-arid and data-scarce regions, where high-resolution DEMs are often unavailable.

• Analyzed GNSS calibration effectiveness on different InSAR algorithms. • Compared calibrated InSAR displacements with submillimeter precision NDTs. • Used polynomial regressions to model and correct InSAR displacement errors. • Validated improvement locally and globally using GNSS and levelling data. • Found linear adjustment to be the best justified option for calibration in most cases. This article presents an analysis of the effectiveness of performing a calibration through Global Navigation Satellite System (GNSS) data on different approaches of the Interferometric Synthetic Aperture Radar (InSAR). InSAR has become a key technique for monitoring ground surface, but its results often exhibit systematic biases and deviations from ground truth measurements. This study addresses the need for more reliable displacement data by evaluating a GNSS-based calibration approach across three InSAR processing approaches: i) Persistent Scattering Interferometry (PSI) method using the open-source software StaMPS, ii) PSI method using the commercial SARPROZ software and iii) the Quasi-PS method implemented with SARPROZ. Sentinel-1 A/B data from both ascending and descending orbits, covering the period from October 2017 to January 2019, were used. Displacement errors from the same points were modeled using polynomial regressions and calibrated using reference data from GNSS and levelling, both with submillimeter precision. The effectiveness of the calibration was assessed at two levels: locally (via a corner reflector) and globally (via a GNSS baseline several kilometers long). Results show that 25 out of 27 displacement time series required correction in the mean value, with StaMPS showing the greatest need for calibration. The calibration improved the original results at all scales and acted mainly on the mean difference, with linear calibration being the most robust and consistent option. These findings highlight the importance of incorporating ground truth data to enhance InSAR reliability and support its application in geodetic and structural monitoring.

Cyclist safety remains a critical issue in urban transportation, where infrastructure configuration and spatial dynamics play a key role in crash occurrence. This study estimates cyclist crash frequencies in Lisbon between 2015 and 2019 using Poisson-Tweedie models, which are well-suited for overdispersed count data. A total of 541 cyclist crashes were analyzed, spatially structured into 250×250 meter grid cells and supplemented with covariates such as road length, intersection types, and various cycling infrastructure elements. Two models were developed: a base model with aggregated variables and a disaggregated model distinguishing road types, intersection forms, and cycleway categories. Both models incorporated spatial autocorrelation to account for neighboring effects. The key findings indicate that intersection density and road length are strongly associated with crash frequency, while cycleway length has a more modest yet significant effect. The disaggregated model offers greater interpretability but does not outperform the base model in predictive accuracy or goodness-of-fit, suggesting that a simpler specification may be more effective for policy applications. Elasticity analysis revealed that intersections have the greatest influence on crash risk, followed by road length and cycleways. Spatial predictions aligned with observed crash clusters and highlighted latent high-risk zones, reinforcing the model's utility for proactive safety planning. The study concludes that improving intersection design is likely to yield greater safety benefits than merely increasing cycling infrastructure length. These results provide actionable insights for data-driven urban mobility planning and emphasize the value of predictive modeling tools for cyclist safety management.

Embankments such as fluvial dikes or dams may fail due to breaching caused by overtopping or internal erosion. The mechanisms associated with the temporal evolution of embankment breaching are still not fully understood. A deeper knowledge can be attained if novel cost-effective experimental techniques are applied in laboratory studies. Contributing to this general aim, this study presents the application of light detection and ranging (LiDAR) technology in medium-scale experimental models of earth dams and fluvial dikes failures. This technology enables the complete 3D digital reproduction of the embankment structures at various stages of failure using point clouds generated by a sensor with an integrated depth camera operating in the near infrared spectrum and using the time-of-flight (ToF) principle. This study is aimed at discussing the errors associated with the ToF principle, through a benchmark analysis involving structure from motion (SfM), and at discussing its application to real-time laboratory-scale embankment breaching. Emphasis is placed on the challenges associated with mapping the water surface and refractive index correction when the structures are submerged. The applications show the potential of ToF-based real-time monitoring to quantify the volumes of material eroded by hydraulic erosion and mass detachments. Two different laboratory embankment overtopping experiments were conducted to verify the ToF principle. In the first, the flow was stopped at different instants, and the reservoir was drained to allow scanning for 3D digital reconstitution. In the second, a dike breach evolution was continuously scanned at five-point clouds per second to enable near-continuous 3D reconstitutions. This study shows that SfM yields the highest point density per measurement, but that ToF has the same measurement error range. Unlike SfM, ToF enables realistic real-time characterization and quantification of mass detachments, allows for reconstruction of submerged 3D structures, and enables precise identification of the water-free surface position.

Abstract Road pavements are vital for transportation infrastructure, yet they deteriorate over time due to traffic loads and environmental factors, resulting in cracks and damage. This paper introduces an innovative method for crack detection on road pavements using digital imagery. Our approach incorporates geo-localization, annotates, characterizes, and quantifies crack severity. This empowers experts to monitor crack progression, a critical element in pavement management. The methodology allows for seamless result comparison and augments existing techniques, aiding in condition assessment and conservation strategy determination. Timely detection of cracks enables proactive maintenance, preventing structural degradation, and ensuring user safety and comfort. Leveraging deep learning and open-source frameworks like TensorFlow and QGIS, our approach automates road pavement image analysis and crack identification, providing a cost-effective, accessible solution for crack detection. This research offers significant advantages in resource efficiency and accessibility, especially in areas without regular manual inspections or dedicated vehicles, thereby enhancing road pavement monitoring and maintenance.