ce/papers最新文献

[Abstract: 1300 characters maximum] Seismic reliability of aging infrastructure, especially bridges, is crucial in earthquake-prone regions. Environmental factors like CO2 and chlorides can significantly worsen deterioration, requiring to schedule over time combined rehabilitation/retrofitting interventions, e.g. with composite materials. In this context, bridge owners need reliability-based metrics to schedule timely interventions. The present study examines the use of fabric reinforced cementitious matrix (FRCM) systems for seismic retrofitting of a common existing bridge type in Italian infrastructure. The research involves deriving fragility curves for the “as built” configuration, and for a series of deterioration scenarios and FRCM retrofitting schemes. Time-variant seismic reliability profiles are then assessed, quantifying reliability gains from FRCM retrofitting at different time instants.The results provide valuable information to support infrastructure owners in decision-making regarding seismic retrofitting strategies and scheduling of interventions, ensuring optimal bridge management and enhanced seismic resilience.

Structural health monitoring and damage detection methods often rely on numerical models to interpret recorded data. These models, however, are frequently subject to inaccuracies and require validation using empirical data that is inherently uncertain. To address this challenge, researchers and practitioners commonly employ Bayesian model updating techniques, utilizing Markov Chain Monte Carlo methods to sample from the posterior distribution. These approaches are valued for their robustness and flexibility.

Recent advancements in model reduction and surrogate modeling have further enhanced the efficiency and accuracy of Bayesian updating methods. In this contribution, we present two such approaches: a Kalman filter-based method and a transport map-based method, both incorporating the generalized Polynomial Chaos Expansion. The performance of these methods is demonstrated through the updating of model parameters for a wooden frame structure based on measured natural frequencies.

Large structures as part of the transport or energy infrastructure are inspected visually or manually using methods such as the knock test to reduce the likelihood of structural failure. Today, more advanced methods are only used in exceptional cases and locally. In addition to the size and poor accessibility, the individuality of the structures due to large manufacturing tolerances has so far prevented automated recording and evaluation. Two automation solutions are presented here that address these challenges of large structures. The ARGOS system is used for the automated inspection of wind turbine rotor blades and combines active thermography with geometry and surface capture. This enables a comprehensive analysis of the outer and inner structure. The second application, the LaserBeat system, is designed for the inspection of concrete structures, in particular the tunnel lining. Optical methods are also used here to capture the geometry and surface. However, hidden defects are detected by scanning using a pulse laser and laser vibrometer. In addition to reproducible data acquisition, these approaches also make it possible to objectify the condition assessment. The systematic use of risk management procedures and reliability analyses is only possible as a result of this improved data situation.

The integration of Building Information Modeling (BIM) into automated manufacturing processes creates new synergies between robotics and monitoring systems. A precise 3D building model ensures the exact positioning of components and forms the basis for controlling robotic systems. To enable a seamless workflow, precise geometric data and a clearly defined execution sequence must be established during the early planning phase. While robots operate with submillimeter accuracy under ideal conditions, external factors—such as defective components or incorrect deliveries—can lead to discrepancies between the intended and actual state. The integration of execution and real-time monitoring within robotic systems enables continuous micro-monitoring, facilitating rapid error detection and correction. Moreover, the automated acquisition and comparison of actual data within the BIM framework enhances the timeliness and integration of all process-relevant information, ensuring that all associated parameters dynamically adjust in response to disruptions. This interplay not only improves manufacturing quality but also mitigates systemic weaknesses and increases the overall level of automation.

When analyzing the distribution of climate, health or similar risk-related data in the built environment, we often involve spatial heatmaps that are placed over or between existing environmental geometry. Common examples of this are indoor air-quality visualizations or city-scale maps of flood risk. These heatmaps can be based on simulations, interpolated measurement data, or other probabilistic methods that turn limited data into full spatial coverages. This means that beyond the visualized risk factor, there is always a measure of uncertainty in the data. While there has been research into showing this uncertainty on spatial heatmaps, such techniques have rarely been applied in urban scenarios with detailed building geometries. These environments introduce occlusion and other viewpoint-related issues and thus make existing cartographic techniques less effective. In this paper, we want to develop visualization strategies that effectively show uncertainty on spatial heatmaps, while circumventing issues of occlusion and viewpoint-dependency. To do so we collect common uncertainty visualization methods from the literature and conduct a preselection for this use case. We then evaluate the effectiveness of each method based on an example scenario, discussing any performance and readability issues that arise. Finally, we recommend certain configurations of methods that strike an appropriate balance between the chosen quality measures.

Precisely assessing the reliability of adhesive bonds is crucial for both ensuring structural safety and reducing mass. Classical design and dimensioning methods often rely on fixed, empirically derived safety margins combined with deterministic models, which can lead to overly conservative and inefficient designs. Models based on modern crack initiation theories, such as finite fracture mechanics, offer a physics-based approach to understand the effect of uncertainties. In this study we make use of a highly efficient finite fracture mechanics solution for the analysis of adhesive joints with brittle, structural adhesives to study the effect of a large set of geometrical and material properties as well as geometrical nonlinearity and thermal residual stresses. The model allows the quantification of uncertainties stemming from variability in input parameters using probabilistic approaches. By defining survival probabilities, the results are related to conventional safety factor methods, providing insights into the limitations of empirical approaches. Furthermore, we analyze the effect of thermal eigenstrains on the nonlinear fracture analysis and make use of a clustering of individual uncertainties to discuss partial safety factors.

Unreinforced masonry (URM) buildings constitute an important portion of the European building stock, where metal injection anchors are widely utilized for seismic retrofitting. While crack propagation in URM walls is extensively studied, the interaction between these cracks and structural anchors remains an underexplored area of research. This paper presents a probabilistic computational framework to serve as the foundation for investigating these crack-anchor interactions. The model simulates the progressive failure of a URM wall under diagonal compression using a two-dimensional discrete element approach implemented in MATLAB. The wall is discretised into brick and joint mortar grains, and the boundaries between them are assigned unique failure thresholds sampled from Weibull distributions specific to each pair of grain types constituting the boundary (Brick-Brick, Joint-Joint, and Joint-Brick). A novel crack propagation algorithm guides the formation of the crack pattern. A large ensemble of simulations was performed to generate a probabilistic crack map, which reveals a distinct diagonal shear band. The results quantitatively show that the failure is dominated by cracking at the joint-brick interfaces, which are shown to be the most probable locations for crack occurrence. The resulting heatmap provides a quantitative tool for identifying regions susceptible to crack occurrence, forming the basis for future investigations into anchor performance in cracked masonry.

Power Actuated Fasteners (PAFs) are widely used in construction for non-structural, light-duty applications due to their efficient and cost-effective installation. These fasteners are often installed in sets to improve reliability. This study focuses on predicting the pull-out capacity of individual PAFs based on experimental measurements using a machine learning approach. A Random Forest model is developed and trained on an extensive dataset of test results conducted across various concrete configurations, including both traditional concrete and fiber-reinforced concrete, using steel and synthetic fibers. Key experimental parameters such as fiber type and dosage, nail curvature, embedment depth, and surface damage characteristics are incorporated into the model. The model is thoroughly tested, and its predictive performance evaluated using standard metrics such as MAE, MSE, RMSE, and R². The results demonstrate the model's ability to capture complex relationships between the input parameters and the pull-out capacity, offering an interpretable and data-driven tool for estimating fastener performance. This approach enhances the reliability of fastening systems by enabling performance assessment based on measurable input parameters—without the need for additional destructive testing. The methodology can be extended to other fastening technologies and construction scenarios, contributing to safer and more reliable structural design.

Building design is strongly influenced by regional contexts, including climatic conditions, structural requirements, and cultural preferences. These factors impact material selection and pose challenges to the transferability of building typologies and material indicators (MIs) across regions. This study examines how regional context affects design and material requirements, using a 9-story residential building as a case study based on building standards in Kazakhstan and Germany. The building was chosen for comparability, reflecting cultural differences in construction methods, such as with and without a basement. Climatic differences affect building physics requirements, influencing the material needs of the building envelope. Structural design followed reinforced concrete standards in both countries, determining material quantities. Analysis identified key factors influencing the transferability of building concepts, particularly climatic conditions—Kazakhstan's colder winters require thicker insulation walls—and Germany's cultural preference for basements, leading to higher material consumption. The study emphasizes the need for region-specific approaches to sustainable, climate-resilient design and strategies to improve the transferability of MIs across regions.

Seismic isolation and damping technologies, utilizing damping properties and energy dissipation of devices, significantly reduce displacements and forces in bridge systems during earthquakes, becoming crucial for bridge seismic resistance. This study examines the principles, design, and applications of viscous dampers in large-span arch bridges, showing their effectiveness in enhancing seismic performance under extreme events. It suggests that integrating various isolation and energy dissipation methods can greatly expand seismic bridge design possibilities.