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Performance Prediction of Anchors in SFRC using Minimally Invasive and Non‐Destructive Techniques
(2023)
AbstractFastenings are crucial for the upgrade of existing structures, by either enhancing structural components or modernising and expanding structures and structural systems. Whilst concrete is prevailing in the existing building stock, new types of concrete composites are increasingly used to improve the sustainability and overall structural performance of built assets. The addition of short dispersed fibres is a material technology with several applications. This paper addresses the synergy of fastening elements, introducing localised loads in concrete substrates, and of fibres, particularly in terms of the fibre orientation and density in the load application area, which is a significant quality aspect. The possibility to forecast this synergy based on non‐destructive or minimally invasive testing methods is an efficient tool to increase confidence in the design load‐bearing performance of the anchorages. Initially, previous literature and own investigations on such techniques are presented. Furthermore, a methodology is presented on associating such information to predict a single anchor's resistance on the basis of sophisticated non‐linear analyses and testing with engineered fibres alignments.
Machine learning in non-destructive testing (NDT) offers significant potential for efficient daily data analysis and uncovering previously unknown relationships in persistent problems. However, its successful application heavily depends on the availability of a diverse and well-labeled training dataset, which is often lacking, raising questions about the transferability of trained algorithms to new datasets. To examine this issue closely, the authors applied classifiers trained with laboratory Ground Penetrating Radar (GPR) data to categorize on-site moisture damage in layered building floors. The investigations were conducted at five different locations in Germany. For reference, cores were taken at each measurement point and labeled as (i) dry, (ii) with insulation damage, or (iii) with screed damage. Compared to the accuracies of 84 % to 90 % within the laboratory training data (504 B-Scans), the classifiers achieved a lower overall accuracy of 53 % for on-site data (72 B-Scans). This discrepancy is mainly attributable to a significantly higher dynamic of all signal features extracted from on-site measurements compared to laboratory training data. Nevertheless, this study highlights the promising sensitivity of GPR for identifying individual damage cases. In particular the results showing insulation damage, which cannot be detected by any other non-destructive method, revealed characteristic patterns. The accurate interpretation of such results still depends on trained personnel, whereby fully automated approaches would require a larger and diverse on-site data set. Until then, the findings of this work contribute to a more reliable analysis of moisture damage in building floors using GPR and offer practical insights into applying machine learning to non-destructive testing for civil engineering (NDT-CE).
We introduce the REINCARNATE project, funded by the European Union's Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70% respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices.
Das EU-finanzierte Projekt REINCARNATE, im Rahmen des Horizont Europa Programms, setzt neue Maßstäbe in der Kreislaufwirtschaft des Bauwesens durch den Einsatz fortschrittlicher Bauwerksdiagnose-Tools. Diese Werkzeuge, insbesondere die Zerstörungsfreie Prüfung (ZfP), spielen eine zentrale Rolle bei der Förderung der Zirkularität in der Baubranche. Die ZfP ermöglicht es, die Qualität und Eignung von Baumaterialien präzise zu bestimmen, ohne diese zu beschädigen. Dies ist entscheidend für die Wiederverwendung und das Recycling von Baustoffen und trägt wesentlich zur Reduzierung des Bauabfalls bei. Durch die Integration dieser Technologie in die Planungs- und Entwurfsphase können Architekten und Ingenieure aktiv einen nachhaltigeren Bauprozess fördern. REINCARNATE verbindet diese Diagnosemethoden mit digitalen Innovationen wie der Technologie des digitalen Zwillings und KI-gesteuerten Lösungen. Diese Kombination ermöglicht eine noch genauere und effizientere Bewertung und Nutzung von Baustoffen, was die Wiederverwendbarkeit und das Recycling verbessert. Im Rahmen des Projekts werden elf Demonstrationsprojekte in ganz Europa umgesetzt, die die praktische Anwendung dieser Technologien zeigen. Diese Projekte demonstrieren, wie ZfP und digitale Werkzeuge zusammenwirken, um Materialien, die traditionell als Abfall angesehen werden, in wertvolle Ressourcen umzuwandeln. Das Ziel von REINCARNATE ist eine 80-prozentige Verringerung der Bauabfälle und eine 70-prozentige Reduzierung des CO2-Fußabdrucks. Mit diesem Beitrag demonstrieren wir eine klare, umsetzbare Anleitung für die Bauindustrie, wie Innovationen in die Praxis umgesetzt werden können. Wir präsentieren greifbare Strategien zur Implementierung nachhaltiger, zirkulärer Praktiken im Bauwesen und markiert einen Wendepunkt in Richtung einer nachhaltigeren Bauindustrie.
The strength of age-hardenable aluminum alloys is based on the controlled formation of nm-sized precipitates, which represent obstacles to dislocation movement. Transmission electron microscopy (TEM) is generally used to identify precipitate types and orientations and to determine their size. This geometric quantification (e.g., length, diameter) is often performed by manual image analysis, which is very time consuming and sometimes poses reproducibility problems. The present work aims at the digital representation of this characterization method by proposing an automatable digital approach. Based on DF-TEM images of different precipitation states of alloy EN AW-2618A, a modularizable digital workflow is described for the quantitative analysis of precipitate dimensions. The integration of this workflow into a data pipeline concept is also presented. The semantic structuring of data allows data to be shared and reused for other applications and purposes, which enables interoperability.
This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material's pore space. Specific NMR features are then integrated into the material design model, with the model's performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating that NMR-based, low-fidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures.
Related work
Laboratory Study:
Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors
https://doi.org/10.3390/app11198820
On-Site Study:
TBA
Doctoral Thesis:
Non-destructive classification of moisture deterioration in layered building floors using ground penetrating radar
https://doi.org/10.14279/depositonce-19306
Measurement Parameters
The GPR measurements were carried out with the SIR 20 from GSSI and a 2 GHz antenna pair (bandwidth 1 GHz to 3 GHz) in common-offset configuration. Each B-Scan consists of N A-Scans, each including 512 samples of a 11 ns time window. Survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan No Gains were applied.
Folder Description:
Lab_dry, Lab_insulDamage, Lab_screedDamage
- each contain 168 Measurements (B-Scans) in .csv on 84 dry floors, floors with insulation damage and screed damage.
- each floor setup was measured twice on two orthogonal survey lines, indicated by _Line1_ and _Line2_ in the file name.
- the file names encode the building floor setup e.g. CT50XP100 describes a 50 mm cement screed with 100 mm extruded polystyrene below
- the material codes are
CT: cement screed, CA: anhydrite screed, EP: expanded polystyrene, XP: extruded polystyrene, GW: glass wool, PS: perlites
further information can be found in the publication https://doi.org/10.3390/app11198820
OnSite_
- 5 folders containing B-Scans on 5 different practical moisture damages
- the building floor setup is encoded according to the lab with an additional measurement point numbering at the start and a damage case annotation at the end of the file name with _dry, _insulationDamage and_screedDamage
File Description:
B-Scans, Measurement files - no header
- dimension: 512 x N data point with N beeing the number of A-Scans including 512 samples of a 11 ns time window.
- survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan
Moisture References
- Moist_Reference of On-Site Locations include the columns MeasPoint: Measurement point, wt%Screed: moisture content of screed layer in mass percent; wt%Insul: moisture content of insulation layer in mass percent. References were obtained by drilling cores with 68 mm diameter in the center of each survey line.
- Moist_Reference_Screed of Lab data include the columns Screed: Screed material and thickness in mm, wt%Screed moisture content of screed layer in mass percent
- Moist Reference_Insul of Lab data include the columns Insulation: Insulation material and thickness in mm, water addition in l: water added to the insulation layer in liters, V%Insulation: water added to the insulation layer in volume percent, RH%: resulting relative humidy in the insulation layer during measurement. These References are only avaible for Lab measurements on insulation damages.
An observation of the fracture process in front of the crack tip inside a dentin sample by means of ex-situ X-ray computed tomography after uniaxial compression at different deformation values was carried out in this work. This ex-situ approach allowed the microstructure and fracturing process of human dentin to be observed during loading. No cracks are observed up to the middle part of the irreversible deformation in the samples at least visible at 0.4μm resolution. First cracks appeared before the mechanical stress reached the compression strength. The growth of the cracks is realized by connecting the main cracks with satellite cracks that lie ahead of the main crack tip and parallel its trajectory. When under the stress load the deformation in the sample exceeds the deformation at the compression strength of dentin, an appearance of micro-cracks in front of the main cracks is observed. The micro-cracks are inclined (~60°) to the trajectory of the main cracks. The further growth of the main cracks is not realized due to the junction with the micro-cracks; we assume that the micro-cracks dissipate the energy of the main crack and suppressed its growth. These micro-cracks serve as additional stress accommodations, therefore the samples do not break apart after the compression test, as it is usually observed under bending and tension tests.
AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.
To evaluate the durability of new alternative cement compositions, it is important to examine the internal transport of moisture through these materials. For this purpose, mortars were prepared from different types of cement and capillary suction experiments were carried out. The moisture transport was studied with an NMR tomograph and compared with weight easurements. With the tomograph, the total moisture input could be determined, as well as the moisture content within the samples non-destructively and spatially resolved. This allows precise observation of the moisture fronts. The tomograph was also used to determine the capillary transport
coefficient.