Open Access

This repository includes data derived from raw data (10.5281/zenodo.17184214) and presented in an Excel file. The structure of the Excel file is strongly connected to the presented data evaluation code. The results of the data and code are presented in a linked publication. Data are aquired 04.2024, processed 05.2025 and submitted for publication 04.2026. - Data: Analyzed Imaging PAM-F data of the subaerial green alga Jaagichlorella sp. - inouclated on three different materials - inoculated in two different concentration ranges - measured with different software measurement settings - Code: Custumisable Python code allows a tailoring to the specific experimental design. For each given combination of software measurement setting, algal inoculation and material: - Checks for data quality - Asesses detection limits - Performs linear regressions Based on this, the best software measuement setting for a combination of material and inoculation range is selected and linear calibration curves are created.

Despite the growing interest in applying artificial intelligence (AI) in civil engineering, its use for evaluating concrete properties remains relatively underexplored. In particular, the assessment of air permeability, a key parameter for concrete durability and long-term performance, has not been extensively addressed using AI-based approaches. Traditional methods, such as the Torrent test, provide reliable measurements but are time-consuming, laborintensive, and require specialized equipment. In this study, an image-based deep learning framework was employed, where surface images of concrete specimens served as input data, and the air permeability coefficient kT, measured using the Torrent tester, was used as ground truth. Concrete mixtures were categorized into two classes: “Poor” (low quality) and “Very Poor” (very low quality). Nine batches of cement-based concrete mixtures were prepared, varying in maximum aggregate size and the dosage of air-entraining agents (LP). Deep learning models were developed to link visual surface features with the corresponding air permeability classes. Model performance was evaluated using a combination of statistical measures, including accuracy, precision, recall, F1-score, confusion matrices, ROC-AUC, and PR-AUC, computed across all folds of a 10-fold cross-validation procedure. One-way ANOVA and Tukey's HSD post-hoc test were applied to verify the statistical significance of performance differences. For models achieving the best performance, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to highlight image regions that most strongly influenced the CNN predictions, providing visual insight into the learned feature representations. The results demonstrated that the ResNet50 architecture achieved the most reliable classification performance, highlighting the potential of image-based AI approaches for non-destructive, automated, and field-applicable assessment of concrete air permeability.

Pilze gehören zu den erfolgreichsten Besiedlern harter, nährstoffarmer Oberflächen. Besonders melanisierte schwarze Pilze können extreme Bedingungen wie UV-Strahlung, Austrocknung, Temperaturschwankungen und Schadstoffbelastungen überstehen. Diese Organismen prägen subaerische Biofilme auf Gesteinen, Bauwerken, Solaranlagen und anderen technischen Materialien. Der Vortrag gibt einen Überblick über die Ökologie, Biodiversität und Anpassungsstrategien dieser extremotoleranten Pilze. Anhand des Modellorganismus Knufia petricola werden aktuelle Erkenntnisse aus Genomik und funktioneller Genetik vorgestellt, die zum Verständnis der Besiedlung mineralischer und technischer Oberflächen beitragen. Zudem wird gezeigt, wie Pilzbiofilme Materialeigenschaften beeinflussen, zur Verschmutzung technischer Oberflächen beitragen und als Modell- und Referenzsysteme für Materialforschung und Monitoring genutzt werden können.

Microbiologically influenced corrosion (MIC) remains one of the least predictable and most challenging forms of corrosion despite decades of intensive research. Traditionally, MIC has largely been investigated by identifying individual microorganisms associated with material degradation, with sulfate-reducing bacteria (SRB) dominating both scientific research and industrial practice. While this organism-centred perspective has provided important mechanistic insights, it only partially explains the complexity of MIC observed in real engineering systems. Over the past decade, research on methanogen-induced MIC has challenged several long-standing assumptions and contributed to a broader understanding of microbially driven corrosion [1]. These studies demonstrated that microorganisms previously considered of minor relevance can substantially accelerate corrosion under specific environmental conditions. More importantly, they revealed that microbial identity alone is insufficient to explain MIC. Instead, corrosion emerges from dynamic interactions between microbial communities, biofilms, engineering materials and their surrounding environment [2]. Drawing on research spanning methanogen-induced corrosion mechanisms, dynamic flow systems, biofilm–material interactions and recent developments in realistic MIC testing, this contribution reflects on how our understanding of MIC has evolved over the last decade. It illustrates how the field has progressively moved from simplified laboratory experiments towards experimental approaches that better reproduce the complexity and dynamics of industrial and natural environments [3]. Looking ahead, climate change, environmental pollution, ageing infrastructure and the global energy transition are expected to further increase the relevance of MIC across a wide range of industrial sectors. Addressing these challenges will require realistic testing strategies, harmonised FAIR datasets and closer integration of microbiology, corrosion science, materials engineering and data science to enable predictive corrosion management. Rather than asking which microorganism causes corrosion, future MIC research should increasingly focus on under which environmental conditions microbial communities become corrosive. This conceptual shift provides an important foundation for developing the next generation of predictive and sustainable MIC management strategies.

The development of environmental measurement systems for mobile platforms is often time-consuming and Limits their rapid use in research applications. This paper presents a modular sensor system designed for flexible integration on unmanned aerial vehicles and ground units. In this project, the system is integrated on a UAV platform for gas sensing applications and consists of electrochemical sensors as well as temperature and relative humidity sensors. To assess the suitability of the sensors for mobile operation, a systematic baseline measurement and environmental characterization were conducted in a climate chamber under varying temperature and humidity conditions. The results show that rapid changes in temperature and humidity cause pronounced transient responses, indicating a strong cross-sensitivity that must be considered in mobile applications, while stable environmental conditions lead to a reliable return to the zero baseline. These findings highlight the importance of environmental stabilization and correction strategies and demonstrate the feasibility of the proposed modular system under controlled conditions, while emphasizing the need for environmental compensation strategies for UAV-based measurements.