Christian Walther

In this paper, a methodology for safety assessment of Carbon Capture and Storage projects is presented, which investigates the long term leakage of the CO2 reservoir in a geological storage site. Based on a simple, one-dimensional leakage model, the reservoir integrity is investigated by considering uncertain rock layer parameters such as permeability and viscosity. Two well known storage projects, Kezin in Germany and Quest in Canada are investigated exemplarily and the leakage time, when the first stored CO2 reaches the earth ground surface and the CO2 flux could be estimated in a statistical manner. The obtained results could show, that the scatter of the seal layer properties is mainly dominating the variation of the investigated model outputs. Thus, a sufficient accurate knowledge of these parameters is critical for a successful uncertainty quantification of CO2 storage reservoir. The analyses of the storage sites Kezin and Quest show highly different results with respect to the long term safety. The proposed method is a valuable approach for investigations in early conceptional states, since it requires only a few parameters and can directly quantify additional needs for further knowledge gain.

Detecting rare structural damage without labeled fault data remains a critical unsolved challenge in structural health monitoring (SHM). Prestressed concrete catenary poles are key elements of high-speed railway infrastructure, and undetected degradation can compromise safety and service reliability. This paper introduces BcDCGAN, a Bayesian conditional deep convolutional generative adversarial network designed for unsupervised anomaly detection in multivariate vibration time series from three in-service catenary poles. Trained exclusively on healthy acceleration signals with wind-speed conditioning, the model learns the normal structural dynamics and produces an uncertainty-based anomaly score that combines reconstruction quality, adversarial evaluation, and epistemic uncertainty into a single decision function. An adaptive, data-driven threshold estimate from healthy validation data enables practical deployment without damage labels. On a real 2017 catenary pole dataset (1606 signals, 70/10/20 split) with injected, physically motivated damage-like patterns, BcDCGAN achieves high anomaly recall with interpretable uncertainty signals and clear separation between normal and anomalous latent representations. The results suggest that Bayesian conditional GANs can support risk-aware monitoring of railway infrastructure under varying environmental and operational conditions.

Detecting rare structural damage without labeled fault data remains a critical unsolved challenge in structural health monitoring (SHM). This paper introduces BcDCGAN, a Bayesian conditional deep convolutional generative adversarial network designed for unsupervised anomaly detection in multivariate vibration time series from prestressed concrete catenary poles. The architecture integrates variational Bayesian inference over generator and critic weights with temporal convolutional networks, enabling epistemic uncertainty alongside reconstruction and critic objectives. Trained exclusively on healthy acceleration signals with wind speed conditioning, the model produces a log-space Bayesian anomaly score that jointly combines normalized reconstruction error, critic evaluation, and epistemic uncertainty estimates into a single weighted decision function. An adaptive threshold is calibrated from the validation data for deployment-ready performance. Evaluation on a real 2017 catenary pole dataset (1606 signals, 70/10/20 split) with injected anomalies achieves 99.2% recall while revealing clear latent space separation and appropriate uncertainty signaling for out-of-distribution samples. Progressive posterior uncertainty reduction during training confirms robust learning of healthy structural dynamics, supporting interpretable, risk-aware decisions in safety-critical railway infrastructure.

The topology optimization of reinforced concrete (RC) building frames is relatively underexplored compared to steel structures, partly due to the lack of a systematic approach to generate and select ground structures (GS). Existing methods often use less systematic GS strategies, limiting efficient exploration of the vast and sparse design space shaped by large bay widths and story heights. This work addresses this gap by providing a comprehensive and systematic pipeline tailored for RC frames. The key contributions are: (1) development of a GS generation framework that systematically enumerates all feasible RC frame configurations within user-defined constraints, (2) introduction of a candidate GS selection map, a surrogate-based tool employing graph-based Latin Hypercube Sampling (LHS) and sparse Gaussian Process (GP) models, which predicts compliance early and strategically guides candidate selection, significantly reducing computational cost while serving as a reference for understanding design parameter influences; and (3) implementation of an integrated topology optimization pipeline applying particle swarm optimization (PSO) to selected candidates, achieving efficient compliance minimization with reduced computational effort. The complete workflow - which spans GS generation, surrogate-based candidate selection, and iterative optimization - is implemented and validated in two design domains with width-to-height aspect ratios of 1:1 and 1:1.5 and generates 438,984 and 104,032 different frame configurations respectively. The selected candidates undergo PSO-based optimization, yielding designs with volume fractions below 0.55 and preserving critical floor beams, demonstrating the framework’s ability to enable the design of structurally efficient RC frames. The framework is designed to be extensible, with direct applicability to broader RC design scenarios including three-dimensional frames and nonlinear analysis in future work.

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

Single-board computers, such as Raspberry Pi, have enabled a wide variety of monitoring applications. Microcontroller development boards offer a similar functionality but at a lower cost. Still, they are not widely used, because they are supposed to be slow. This paper aims to evaluate the abilities of modern microcontrollers and compares them with those of single-board computers. For this purpose, experiments concerning power consumption and performance evaluation are conducted. The results suggest that promising low-cost microcontrollers exist, which could be applied to reduce cost and energy consumption per node or increase the number of nodes used simultaneously in one monitoring system.

Influx of Ca²⁺ and Na⁺ ions during an action potential can strongly affect the repolarization and the fast afterhyperpolarization (fAHP) if a neuron expresses Ca²⁺- and Na⁺-dependent K⁺ currents (K_Ca and K_Na). This applies to cockroach abdominal dorsal unpaired median neurons (DUMs). Here the rapid activation of K_Ca depends mainly on the P/Q-type Ca²⁺ current. Adipokinetic hormones (AKHs)—insect counterparts to mammalian glucagon—mobilize energy reserves but also modulate neuronal activity and lead to enhanced locomotor activity. Cockroach AKH I accelerates spiking and enhances the fAHP of octopaminergic DUM neurons, and it is generally held that enhanced release of the biogenic amine from these and other neurons may lead to general arousal. AKH I modulates the voltage-gated Na⁺ and P/Q-type Ca²⁺ current and the background Ca²⁺ current. Upregulation of P/Q-type Ca²⁺ current increases the K_Ca current, whereas enhanced inactivation of Na⁺ current decreases the K_Na current. We quantified the hormone-induced changes in ion currents in terms of Hodgkin-Huxley models and simulated the resulting activity of DUM neurons. Upregulation of P/Q-type Ca²⁺ and K_Ca current enhanced the hyperpolarization but had a weak effect on spiking. Downregulation of Na⁺ and K_Na current decreased hyperpolarization and slightly accelerated spiking. Superposition of these modulations produced an increase in fAHP while the spike frequency remained unchanged. Only when the upregulation of the pacemaking Ca²⁺ background current was included in the simulated modulation the model reproduced the experimentally observed AKH-I-induced changes. The possible physiological relevance of this dual effect is discussed in respect to transmitter release and synaptic integration.