Civil Engineering Design最新文献

A 1800 m long tunnel is planned in Tokyo, with a section width of 10 m and an excavated cross-section of 70 m² for each of the two tunnels. The ground consists of unconsolidated sand, and the conventional tunneling method will be used. Two main challenges need to be addressed: (1) The tunnel passes near wetlands inhabited by sensitive species and invaluable ecosystems, necessitating hydraulic and perimeter surveys to assess the potential impact of excavation on the environment. To mitigate these effects, the tunnel alignment was planned to avoid sensitive wetland areas. A waterproof structure was also designed, incorporating a 3D shell-spring analysis and reinforced bar placement at critical crossings. A Building/Construction Information Modeling, Management model was also developed for precise planning. (2) The tunnel portal is located near residential areas, requiring careful consideration of the surrounding environment. 3D simulations were conducted to evaluate noise levels in the temporary yard to manage noise pollution, and soundproofing measures were implemented. A 3D noise map was also created to facilitate clear communication with residents during explanatory meetings, ensuring transparency and minimizing disturbance. These solutions ensure the project progresses with minimal environmental and residential impact.

Blockchain technology is a digital decentralized data ledger recording transactions in an encrypted format. Its implementation can potentially hold significant advantages for the built environment, particularly in manufacturing and building product usage aligned with Building Information Modeling (BIM). This paradigm shift toward decentralized transactions can foster security, reliability, and accountability. Hyperledger Fabric (HLF), an enterprise-grade distributed ledger, offers a modular, scalable, and confidential digital framework. This article introduces HLF-based workflows to address inefficiencies in BIM and fastener product lifecycle management, such as fragmented data handling and limited process automation. Leveraging chaincodes linked to BIM models, HLF simplifies, enhances transparency, and automates construction product lifecycle processes. Contract data and execution details are managed through a blockchain stored in a common data environment (CDE) and linked to chaincodes. The article presents the conceptualization and implementation of automated workflows, emphasizing efficiency and transparency. While showcasing successful deployments, it also highlights areas for future improvement and development. The proposed framework represents a pioneering step toward a decentralized cooperative environment in the construction industry, aligning with the transformative potential of blockchain technology.

In this study, an efficient surrogate-assisted grey wolf optimizer (GWO) is presented by combining Kriging-based active learning to identify damages in jacketed platforms based on modal analysis. The use of active learning in parallel with GWO significantly reduced the number of calls to the objective function and increased the accuracy of the algorithm's search in the problem space. The proposed approach was first evaluated on four benchmark problems, and its performance was validated against original GWO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. Then, by generating artificial damage scenarios on a real jacket platform in ABAQUS software, it was evaluated for the identification of damaged members. The results indicated high accuracy in estimation and an appropriate convergence rate in solving the high-dimensional and complicated problem of damage detection of jacketed platforms. In such a way that the error rate of damage severity estimation in scenarios 1 and 2 was, on average, 3% and 5%, respectively. Meanwhile, the damage position was correctly estimated, and the call rate of the function was reduced by 50%. The efficiency of the proposed approach shows that it can be used for further works on the reliability-based design of jacket structures.

Infrastructure, especially railway tunnels, require continuous inspection to ensure their safety. Furthermore, the detection of damages at an early stage can improve their service life. These tasks represent a major challenge for those involved and the tunnel down time is a disruption in operation. In rail networks, comprehensive preparations are necessary to be able to plan closures and required compensation measures. To optimize this process, a workflow for a digital inspection was generated to allow a rapid localization and a corresponding time advantage when conducting the activities on site. In doing so, a multitude of bases can be used, a standardization of damage patterns is carried out and a collaborative cooperation of multiple inspection personnel is possible. Depending on the data stock, digitized as-built models in the form of a plan, digital plan documents and existing or generated digital twins can be used. Making it possible to locate a damage already during the inspection and thus enable a comprehensible documentation and an automated generation of reports. The pre-set options for attribution of damages enables a time-optimized inspection on site—which enables a reduction of the required time, the associated restrictions on traffic routing and at the same time reduces the susceptibility to errors. Within this paper, results of a first series of field tests along the Tauern Line of ÖBB, using a digital inspection workflow, are presented.

This paper summarizes current developments and outlines essential teaching content in the field of non-destructive testing in civil engineering for academic education at German-speaking universities. This first memorandum on teaching and research in non-destructive testing in civil engineering (NDT-CE) at German-speaking universities provides an overview of academic education and identifies focal points in teaching and important developments and topics in NDT-CE research. Suggestions are given for the development and advancement of teaching curricula with regard to a comprehensive and sound professional education of students of civil engineering and related disciplines. In terms of content and form, the contribution deliberately borrows from the memorandum of university teachers in the adjacent subject “Building Materials” (Breit et al. Hochschullehrer-Memorandum Werkstoffe im Bauwesen—universitäre Lehre und Forschung, Bauingenieur, 87, 72–80, 2012).

This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk of catastrophic failures. To enhance the accuracy of classification problems associated with liquefaction, we employ ensemble methods, leveraging diverse machine learning techniques such as support vector machines, stochastic gradient descent, multi-layer perceptron neural networks, K-nearest neighbors, and decision trees. The research encompasses data exploration and a subsequent division for performance assessment, followed by hyperparameter tuning through GridSearchCV to optimize model effectiveness. Among the ensemble methods employed, AdaBoost stands out as the most accurate, achieving precision of 85%, recall of 84%, F1 score of 83%, Jaccard index of 72%, and overall accuracy of 84%. However, K-nearest neighbors and decision trees exhibit higher false negative values compared to other methods. Notably, both ensemble approaches provide acceptable estimations, with false negative values ranging from 0 to 1 and false positive values between 7 and 10. The decision tree, while predicting the lowest false positive rate, has a higher false negative count, rendering it less favorable for practical applications.

Since 2023, Austrian Federal Railways (ÖBB), with BEMO-ÖSTU STETTIN, has been constructing the 2.6 km raw construction tunnel Angath (Rohbaustollen Angath) along the Angerberg. It will support geological investigations, logistics for the future main railway tunnel Angerberg, and act as a rescue tunnel, ensuring safety. The project includes a pre-cut, tunnel excavation, and a 110-meter section of the main railway tunnel, with turning niches and cross adits. Building information modeling (BIM) is used for design, tendering, control, and documentation. The integrated project delivery (IPD) model ensures shared responsibility for risks and costs. BIM optimizes data flow and transparency, with tunneling documentation collected on-site, verified in ÖBB's Common Data Environment (MSP), and used for parametric modeling. Cost and progress monitoring occur via software RIB iTWO (Version 2023, 2023). Project documentation and the linking of the documentation into a 3D BIM model of a tunnel enables early detection of deviations and appropriate action to be taken, with transparency and traceability being critical to the success of the IPD project.

Tunnel boring machines (TBMs) have revolutionized underground excavation, offering efficient, and cost-effective solutions for tunneling projects worldwide. With an increasing demand for tunnels in geologically challenging or very hard and abrasive conditions, understanding the critical aspects of cutterhead design and its impact on TBM performance is crucial for successful tunneling operations. Little experience is documented on the performance and durability of TBM operating in “very hard” rock conditions. An unbiased performance quantification is established based on a shared understanding of hard rock conditions. The actual design of TBMs demands can only cover certain aspects of TBM–rock mass interaction, demanding further adoption and improvements of primary and secondary wear.

The Dresden–Prague railway line is an important part of the core TEN-T Orient/Eastern Mediterranean corridor and a major link between Germany and the Czech Republic. The aim of the project is to reduce journey times for international passenger trains and increase capacity for freight trains. At the same time, it will extend the railway network in Central Europe and develop high-speed transport in the Czech Republic. The project will also provide a flood-safe alternative to the Elbe Valley, encouraging a modal shift from road to rail. The core element of this project is the cross-border base tunnel under the Ore Mountains. During the preliminary design, two basic alternatives (“full tunnel” and “partial tunnel”) are being examined. It was announced on November 20, 2023, that the preferred option for the new Dresden–Prague line is the “full tunnel” alternative. For this reason, the article focuses mainly on the technical solutions for the Ore Mountain Tunnel of the “full tunnel” alternative. The BIM methodology was a key element in the preliminary design phase. The model was to be used as the main source of information in the design process and in the development of solutions. It also allowed detailed decisions to be made at an early stage.