Civil Engineering Design最新文献

The sensitivity to reduced installation effort of post-installed fastening systems is determined during the product qualification through robustness tests conducted under controlled laboratory conditions. Using the concept of required α-factors, the installation safety factor γ_inst is determined and provided as an essential characteristic in the corresponding European Technical Assessment (ETA) of the fastener. The purpose of robustness tests is to simulate reduced installation conditions. To gain more insight on the variability of the installation conditions in practice, an in-situ verification of the installation safety factor is carried out on four different construction sites in Austria using four anchorage types to cover all working principles. For this in-situ verification, the obtained ultimate loads from unconfined pull-out tests on-site are compared with corresponding ultimate loads from reference tests under laboratory conditions. Based on the in-situ tests, it is verified if the designed robustness tests are sufficiently appropriate to cover in-situ variations during the installation. The investigation shows that for all tested fastening products, the specified installation safety factor from the ETA could be confirmed with the in-situ tests based on mean values. As expected, the coefficient of variation of the in-situ tests is for torque-controlled mechanical fasteners around two to three times higher than in the laboratory tests, for concrete screws and bonded anchors, the value 1.5 applies.

In the face of increasing construction project complexity and the widespread adoption of Building Information Modeling (BIM), the efficient planning and execution of workspaces have become critical for enhancing productivity, safety, and overall project success. This study presents a BIM-based workspace design tool that addresses the challenges of confined workspaces, concurrent activities, and ergonomically demanding tasks. The tool seamlessly integrates with BIM models to visualize, analyze, and optimize workspace configurations, ensuring that anchor installations, among other tasks, can be completed safely and efficiently. Through systematic research and the application of various use cases, the tool's effectiveness is demonstrated and validated, highlighting its potential to alleviate project managers' workload and significantly improve construction outcomes. The study concludes by outlining future enhancements and providing valuable insights for researchers and practitioners.

Infrastructure management along highways and railways requires inventories of critical structures like retaining walls, which traditionally rely on manual inspection and documentation. Unfortunately, data in infrastructure databases is often incomplete. This study investigates the feasibility of automating retaining wall inventories using public aerial lidar data from the Swiss Federal Office of Topography (Swisstopo) combined with deep learning. We develop a pipeline for data processing and apply the state-of-the-art Superpoint Transformer architecture with SuperCluster for panoptic segmentation. Three distinct approaches are evaluated: transfer learning from general Swisstopo lidar data, transfer learning from the DALES aerial lidar dataset, and training a specialized model from scratch. The specialized model achieves the best performance with 44% Intersection over Union (IoU) for semantic segmentation and 24% panoptic quality on test data. Our findings reveal that the primary challenges stem from data characteristics—like sparse sampling of vertical surfaces due to oblique scanning angles—rather than model architecture limitations. This work provides insights into the development of automated infrastructure inventories and identifies areas for improvement, including the need for expanded training data and robust augmentation techniques.

This study presents an unsupervised machine learning approach to rock mass anomaly detection in tunnel boring machine operational data using a Variational Autoencoder combined with bidirectional Long Short-Term Memory cells. The model was trained on TBM data from the Brenner Base Tunnel project, with minor faults and geotechnically relevant fault zones removed to ensure a rock mass anomaly-free training set. By reconstructing input data from a compressed latent space, the Variational Autoencoder distinguishes between normal and abnormal patterns based on reconstruction errors. Anomalies, such as significant changes in rock mass conditions, are identified when reconstruction errors exceed a threshold based on a skewness-adjusted boxplot. Testing on three distinct sections of TBM data demonstrated the model's effectiveness in reliably detecting minor faults and major fault zones, though occasional delays in detection were noted. This approach underscores the potential of Variational Autoencoder-based rock mass anomaly detection in mechanized tunneling and paves the way for real-time, data-driven decision-making in future TBM tunneling projects.

Condition assessment of masonry lined tunnels is time consuming and labor intensive. Recently developed digital workflows enable structural models to be created automatically, reducing analysis time. As part of these procedures, it is important to be able to identify the location of each masonry block. Masonry joints can be segmented by applying deep learning to 3D point clouds obtained by lidar. However, these models often fail to separate block instances, reducing the effectiveness of subsequent analysis. Recent developments in topological loss functions enable models to more accurately connect detected structures. While these can be applied to better isolate individual masonry blocks, their performance depends on the selected training data, and so further investigation is required to enable the method to be applied effectively to different structures. This study investigates the ability of topological loss functions to enable deep learning models to operate on different tunnels with varying lining properties. By focusing on possible workflows for real world application of these methods, the study shows how training data type and origin impact performance. Block instance segmentation performance is evaluated directly using a new Blockwise Intersection Over Union metric. With this metric, training data volume and variety is shown to be a bigger driver of segmentation performance than either similarity between training and testing datasets or choice of loss function.

The paper presents a detailed assessment of the energy performance of a construction site in Germany, where realistic electricity consumption patterns are obtained using metering devices installed in the site switchgear. The paper analyzes the current operations on construction sites and suggests innovative strategies to improve energy efficiency. The results reveal potential electricity savings of nearly 44% in temporary construction offices. Moreover, the paper discusses various strategies, technological innovations, and management practices that could help reduce electricity consumption while ensuring sustainable and environmentally friendly construction practices.

The German Research Foundation has established the priority program SPP 100+. Its subject is monitoring bridge structures in civil engineering. The data-driven methods cluster deals with the use of measurements and their special global and local analysis methods, which complement each other in an overall multi-scale concept in order to realize condition monitoring. The presented methods aim for damage detection, localization, and quantification of the monitored structure. Static and dynamic investigations based on mechanical multi-scale models were carried out, and process-oriented models combined with image processing methods and machine learning were created. The methods are tested on several laboratory and real-life experimental mechanical structures. The underlying theoretical concept and first experimental results of the research group are presented in this article. This study successfully employs a series of multi-scale experiments, integrating mechanical models and advanced image processing to effectively detect, localize, and quantify damage in bridge structures for enhanced structural health monitoring.

Photogrammetric surveys for geological excavation documentation are standard practice in tunnel construction, benefiting from the sector's ongoing digitalization. However, the application of photogrammetry often remains confined to geological assessments. By creating comprehensive 3D models of excavated tunnels, it is also possible to evaluate engineering aspects. In this respect, the documentation of the as-built condition is of particular interest, that is, the effectively applied shotcrete, which is a novelty application in the field of tunneling. This contribution presents the photogrammetric recording process used in the Angath test gallery, a scientifically monitored gallery that is part of a new railway line in Tyrol, Austria. The evaluation of the overall photogrammetric model and the findings obtained from it are highlighted.

This study presents the innovative application of fiber optic sensors, specifically Fiber Bragg Grating (FBG) sensors, in the Kühtai 2 hydropower station project in Austria. The project involves the construction of a pressure tunnel connecting the Kühtai and Finstertal reservoirs, incorporating concrete linings with and without sealing membrane as well as steel linings. The sensors were accurately installed individually to each lining type, overcoming problem statements such as positioning sensors on unreinforced concrete without damaging the sealing foil or compromising the structural integrity. The monitoring system enables real-time data collection on strain and temperature variations during key phases, including concreting, prestressing, pressure, or filling tests. This approach illustrates the effectiveness of integrating fiber optic technology into hydropower infrastructure, offering enhanced monitoring accuracy and improved decision-making for maintenance and operational strategies. The findings serve as a reference for future projects, presenting the potential of the demonstrated sensor applications in demanding engineering environments.

Achieving robust generalization in machine learning for tunnel boring machine performance prediction is challenging, particularly when models are developed on data from different projects. This study assesses the generalization abilities of K-nearest neighbors, support vector regression, artificial neural networks, random forest, classification and regression trees, and extreme gradient boosting (XGBoost) for predicting penetration rate (PR) and explores the potential of incremental learning to enhance it. The datasets were collected from two tunnels (Line C and Line S) that were constructed in similar geological formation, with similar technology, in Porto, Portugal. In the first part, these models are trained using Line C data and applied to Line S for generalization assess under different splitting and scaling methods. XGBoost demonstrated superior performance in both accuracy and generalization, making it the base model for incremental learning. In the second part, the incremental learning, applied by continually updating the XGBoost model when new data becomes available, was evaluated across different PR ranges and different incremental sizes. Finally, the model trained using Line C data plus Line S data was compared to the model using Line S only to investigate the impact of including Line C data on generalization. Our findings show that for the imbalanced data of PR <125 mm/rpm, incremental learning show unstable generalization but exhibited improved generalization on PR < 25 mm/rpm. For the balanced data of PR <25 mm/rpm, incremental learning showed more stable and gradually improved generalization. Combining Line C data with Line S data for training improved generalization significantly. The study results provide important insights into developing generalization strategies, highlighting the benefits of pre-training with similar data and the challenges of dealing with imbalanced data in real-life projects.