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Onshore wind turbine towers arecommonly designed as hybrid structures,with a prestressed concrete lower sectionand a steel tubular upper section. Thesetall towers are subjected to continuouscyclic loading. Within the joint researchinitiative WinConFat – Structure, BAMis exploring advanced methods tomonitor fatigue damage progression inthe concrete segments of hybrid towers. As a part of WinConFat –Structure, acoustic emission sensorswere installed at the base and at thetransition zone between concrete andsteel of a hybrid tower. Beyond detectionof acoustic emission signals, the sensorconfiguration enables ultrasonic velocitymeasurements around thecircumference. Additional data - such asstrain, temperature, inclination, andacceleration - provide insights intooperational and environmentalinfluences and their effect on theacoustic properties of concrete.
Wind energy plays a substantial role in Germany’s energy transition. In 2006, about 1200 wind turbines systems were installed (BWE (2026)) and will soon reach the end of their service life which is typically 20 years (Ziegler et al. (2018)). Extending their operation requires the development of innovative inspection and monitoring strategies. Onshore wind turbine towers are commonly designed as hybrid structures, with a prestressed concrete lower section and a steel tubular upper section. These tall towers are subjected to continuous cyclic loading. Within the joint research initiative WinConFat – Structure, BAM is exploring advanced methods to monitor fatigue damage progression in the concrete segments of hybrid towers. Previous studies, like Thiele and Pirskawetz (2022) and Frei et al. (2024), indicate that combining ultrasonic testing with acoustic emission monitoring can effectively detect critical conditions as concrete approaches its fatigue limit. As a part of WinConFat – Structure, acoustic Emission sensors were installed at the base and at the transition zone between concrete and steel of a hybrid tower. Beyond detection of acoustic emission signals, the sensor configuration enables ultrasonic velocity measurements around the circumference. Additional data - such as strain, temperature, inclination, and acceleration - provide insights into operational and environmental influences and their effect on the acoustic properties of concrete. This abstract presents acoustic measurements at the transition piece and correlates them with SCADA data, including active power and wind direction.
Wind energy plays a substantial role inGermany’s energy transition. In 2006,about 1200 wind turbines systems wereinstalled (BWE (2026)) and will soonreach the end of their service life whichis typically 20 years (Ziegler et al.(2018)). Extending their operationrequires the development of innovativeinspection and monitoring strategies.Onshore wind turbine towers arecommonly designed as hybrid structures,with a prestressed concrete lower sectionand a steel tubular upper section. Thesetall towers are subjected to continuouscyclic loading. Within the joint researchinitiative WinConFat – Structure, BAMis exploring advanced methods tomonitor fatigue damage progression inthe concrete segments of hybrid towers.Previous studies, like Thiele andPirskawetz (2022) and Frei et al. (2024),indicate that combining ultrasonictesting with acoustic emissionmonitoring can effectively detect criticalconditions as concrete approaches itsfatigue limit. As a part of WinConFat –Structure, acoustic emission sensorswere installed at the base and at thetransition zone between concrete andsteel of a hybrid tower. Beyond detectionof acoustic emission signals, the sensorconfiguration enables ultrasonic velocitymeasurements around thecircumference. Additional data - such asstrain, temperature, inclination, andacceleration - provide insights intooperational and environmentalinfluences and their effect on theacoustic properties of concrete. Thisabstract presents acoustic measurementsat the transition piece and correlatesthem with SCADA data, including activepower and wind direction.
Simulation-based digital twins are emerging as key enablers for extending the service life of bridges through data-driven assessment, performance prediction, and proactive maintenance planning. They integrate a numerical simulation model with sensor observations to estimate condition indicators and predict future performance. Reliable prognostics, however, require that model parameters are continuously updated to reflect the evolving state of the structure. Despite this, assumptions made during model formulation introduce model form uncertainty that leads to systematic discrepancies between predictions and measurements. This contribution presents an embedded approach for quantifying and propagating model form uncertainty in bridge digital twins. The method attaches the uncertainty to the updated parameters during the model updating process, ensuring its consistent propagation to all relevant quantities of interest, including condition indicators and prognostic outcomes. This approach allows predictions to be expressed as probabilistic distributions rather than deterministic scalar values, providing a richer characterization of prediction reliability and improved decision support. By explicitly representing model form uncertainty, the framework enhances both the interpretability and trustworthiness of simulation-based assessments. The methodology is applied to the Nibelungenbrücke in Worms, Germany. Results demonstrate that accounting for model form uncertainty improves agreement between simulated and observed responses and provides more credible prognostics for long-term performance management. The proposed framework contributes to developing reliable and trustworthy digital twins as intelligent tools for extending the operational life of critical infrastructure.
Geotechnical substructures and foundations require substantial amounts of primary resources and contribute significantly to the embodied carbon of buildings, yet circular economy approaches have so far received comparatively little attention in geotechnical engineering. This contribution presents a framework for circular foundation engineering based on five R-strategies: Rethink, Reduce, Reuse, Retrofit, and Recycle. The strategies are applied across material, system, and process levels and include material-efficient foundation design, low- and non-cementitious binders, reuse and non-destructive assessment of existing foundations, data-driven monitoring and life-cycle assessment, and the use of recycled materials in geotechnical applications. Particular emphasis is placed on linking innovative materials, structural concepts, digital methods, and construction and dismantling processes. The proposed framework highlights key research needs for reducing resource consumption and emissions while enabling the long-term reuse and recycling of geotechnical structures and materials.
Geotechnical substructures and foundations require substantial amounts of primary resources and contribute significantly to the embodied carbon of buildings, yet circular economy approaches have so far received comparatively little attention in geotechnical engineering. This contribution presents a framework for circular foundation engineering based on five R-strategies: Rethink, Reduce, Reuse, Retrofit, and Recycle. The strategies are applied across material, system, and process levels and include material-efficient foundation design, low- and non-cementitious binders, reuse and non-destructive assessment of existing foundations, data-driven monitoring and life-cycle assessment, and the use of recycled materials in geotechnical applications. Particular emphasis is placed on linking innovative materials, structural concepts, digital methods, and construction and dismantling processes. The proposed framework highlights key research needs for reducing resource consumption and emissions while enabling the long-term reuse and recycling of geotechnical structures and materials. 10C2CC_2026_TUB_Abstract_Rackwi…
For regenerating the results, the "code" folder contains all required software used to generate them. The data alocated in "inputs" comes from the monitoring system of the Nibelungenbrücke, in Worms (Germany) and it is owned by LBM Landesbetrieb Mobilität Worms and Marx Krontal Partner (MKP) GmbH, who gracefully accepted for its publication. The only exception is the wind data, which was generated using OpenMeteoAPI.
To regenerate the results, install the included environment and run the master workflow using PyDoit. The data generation scripts are included for completeness, but they are commented out as they require access to the API for the monitoring data of the Nibelungenbrücke.
Digital twins of bridges rely on physics-based models to infer full-field structural responses from sparse monitoring data. The reliability of these predictions depends on the calibration of model parameters while accounting for discrepancies between the model and the physical system. Such discrepancies, commonly referred to as model-form uncertainty (MFU), arise from simplifying assumptions and incomplete representations of physical processes, and can substantially affect predictive reliability. Explicitly quantifying MFU during calibration is therefore essential for trustworthy digital-twin predictions. This work presents a calibration framework that explicitly represents MFU through stochastic parameter embedding. The framework is demonstrated using a simplified two-dimensional cross-sectional thermal model of the Nibelungenbrücke calibrated against temperature measurements from its monitoring system. By explicitly accounting for MFU, the proposed methodology enables computationally efficient models to be reliably employed within digital-twin environments while maintaining predictive credibility. The framework introduces a three-step calibration strategy that progressively addresses sources of discrepancy while maintaining a clear distinction between identified uncertainties. Variance decomposition is used to characterize the structure of predictive uncertainty and its propagation to quantities of interest. Remaining discrepancies between predictive distributions and observations are quantified using Kolmogorov-Smirnov metrics to assess predictive consistency across seasonal conditions. The results demonstrate that explicitly accounting for MFU improves the interpretability and reliability of predictive uncertainties and validate the methodology for calibration of physics-based models supporting digital twins in structural health monitoring.
In civil engineering, non-destructive testing (NDT) is well established for structural assessment and geometric inspection. In contrast, non-destructive material characterisation – providing quantitative information on chemical composition, hydration state and pore structure – remains less developed. Yet demand is increasing. Ageing infrastructure requires reliable durability diagnostics to assess degradation mechanisms and the transport of moisture and harmful ions. At the same time, circular construction introduces secondary raw materials and waste-based binder systems whose hydration behaviour and long-term performance are not yet fully understood. These developments call for analytical techniques that link chemical composition, microstructural evolution and durability performance with minimal sampling effort.
Laser-induced breakdown spectroscopy (LIBS) and ¹H nuclear magnetic resonance (NMR) relaxometry offer complementary capabilities to address these challenges. LIBS enables rapid, spatially resolved multi-element analysis via local plasma generation by pulsed laser excitation. Elemental maps and depth profiles can be acquired directly on drill cores or structural surfaces, allowing the investigation of chloride ingress, alkali and sulphate distributions, carbonation fronts and binder composition. The method combines high lateral resolution with multi-element sensitivity, making it suitable for durability-oriented assessment and material classification. Calibration strategies for heterogeneous cementitious matrices, including data-driven approaches, are discussed.
Complementarily, ¹H NMR relaxometry provides non-destructive insight into pore structure development and moisture distribution. Relaxation time distributions enable detailed porosity assessment, monitoring of hydration processes and quantification of moisture transport and damage-related microstructural changes. By linking pore evolution to service-life relevant parameters, NMR contributes directly to durability evaluation.
The growing availability of portable and handheld LIBS and X-ray fluorescence (XRF) systems further expands field applicability, enabling rapid on-site elemental screening for condition assessment, quality control and circular material qualification.
Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid, minimally invasive multi-element atomic emission technique that has seen growing adoption in materials analysis yet remains largely unfamiliar within the construction industry. In LIBS, a focused laser pulse ablates a small amount of material, generating a short-lived plasma whose optical emission encodes the elemental composition of the sample. This enables comprehensive chemical material characterization, including the detection and spatially resolved analysis of relevant elements and the investigation of transport processes, in particular of potentially damaging ions such as chlorides.
This contribution introduces LIBS to a civil engineering audience and demonstrates its potential for the chemical characterization of concrete, with a particular focus on chloride detection. Practical examples demonstrate analyses conducted in accordance with established German standards for chloride determination in concrete using LIBS. A key advantage highlighted is the high-resolution 2D mapping capability of LIBS, which allows the heterogeneous phase composition of concrete to be explicitly accounted for during analysis — an aspect that poses significant challenges to conventional methods.
Owing to its ability to acquire spatially resolved profiles without extensive sample preparation, LIBS enables reliable assessment of chloride ingress and associated transport phenomena. Results are benchmarked against established reference methods, demonstrating competitive accuracy alongside considerable practical benefits in measurement speed and spatial resolution. The potential of LIBS as a complementary diagnostic tool for on-site concrete durability assessment is discussed.
