Civil Engineering Design最新文献

This article investigates the effect of different drilling forces on drill bit wear and their effects on drilling time within the context of the development of a drilling robot for on-site construction applications. The study explores variations in three force levels (80, 120, 160 N), three directional orientations (upwards, downwards, horizontal), four drill bit diameters (8, 10, 12, 16 mm), and two concrete strength types (C25/30, C70/85). The findings indicate that while there is little divergence in drill bit wear across force levels, the influence of concrete strength is undeniable. Furthermore, drilling time increases, by 7 % to 11 % after 37.5 m drilling depth and up to 68% after 150 m depending on the drilling configuration. In conclusion, the study reveals a notable discrepancy between the progressive increase in drilling time and the relatively stable progression of drill bit wear, particularly in achieving code-compliant boreholes.

In practice, various empirical methods such as the flow table test or the slump test are in use worldwide for assessing the workability of fresh concrete on the construction site. The majority of these tests has in common, that fresh concrete is subjected to some kind of defined flow process on a standardized table-like platform and that the geometrical properties of the material after the flow has ceased is determined by simple means such as measuring the flow cake diameter or its sag. The paper at hand proposes a novel image-based approach for an automatic derivation of concrete properties as part of the flow table test. The image-based method enables a digital evaluation of concrete properties. By combining digital image analysis and deep learning methods, not only the consistency but also an abundance of additional concrete properties can be derived from image data. In this way, the quality control of the fresh concrete can be expanded to include a large number of additional parameters, currently not available to the producer nor to the construction site. This data can be integrated into a digital control loop, with which communication between the concrete producer and the construction site can be automated using highly precise real-time data.

For microtunneling in hard rock, as it is encountered very often in alpine tunneling, the continuous further development of technologies plays a key role to overcome the limitations in drive length and to improve performance. During the last decades, the application range of slurry microtunneling has been considerably extended and more demanding projects have been designed for underground utilities installations worldwide, including many sewage tunnels and hydropower plants. The prevailing ground conditions still play a key role and determine the limits for alignment design and the possible use of trenchless construction methods. In hard rock conditions with high rock strength and/or abrasivity, the applicability of slurry microtunneling has been especially limited in the non-accessible diameter range in the past. With the new AVN 800 HR for hard rock microtunneling, Herrenknecht has developed a machine concept, enabling drives of up to 200 m, with precise steering of the MTBM. Longer drives, even through hard rock, make microtunneling a more economic, flexible, and more environmentally friendly construction method. New opportunities for clients and consultants in the planning of tunnel routes in alpine environments for different infrastructure installations can be considered, making trenchless installations even more cost-effective, while improving public acceptance.

Buildings involve multiple participants and materials that must work together throughout their life cycle, from initial planning to decommissioning and recycling. This can create safety concerns, particularly with regard to critical components. Detailed documentation and tracking of product characteristics are necessary, as well as outlining the related obligations of the parties involved. Currently, this problem is often addressed by numerous contracts and paper-based building documentation. Blockchain technology could prove to be a future-oriented solution to such use cases. Additionally, so-called Smart Contracts, which are custom-designed applications running on the given Blockchain platform, can be an appropriate way for documentation in the construction and facility management process since they allow distribution of their execution to the entirety of the involved Blockchain participants. Based on this approach, this paper presents a platform solution that provides up-to-date product information on various components. The outcome is a system that facilitates digital documentation on a secure legal foundation, with an interface tailored to the specific terms and conditions of each partner involved in the construction and maintenance process.

Pre-grouting in hard rock tunneling is crucial for mitigating water ingress, significantly affecting project time and cost. Predicting pre-grouting requirements is challenging and relies heavily on the expertise of on-site personnel for decision-making. This paper explores using supervised machine learning (ML) to create a data-driven pre-grouting decision process, aiming to predict “grouting time” and “total grout take.” Tree-based regression models were developed using data from a Norwegian railway project, including typical tunneling data. These models showed limited predictive performance, with R² scores of 0.40, though a significant relationship was observed. The limited performance highlights the need to identify parameters that significantly impact grouting outcomes rather than indicating the unsuitability of tree-based models. Future research should consider a larger data set and additional parameters, such as more data on rock mass quality, hydrogeological conditions ahead of the face, and human, organizational, and contractual factors. Despite initial findings, supervised ML shows promise in enhancing data-driven decision-making in pre-grouting by using appropriate input features and target variables.

This article takes a further step on the digitalization path in tunneling by implementing the concept of the digital twin and examining its potential at the three levels of real-world integration: digital model, digital shadow, and digital twin (DT). It evaluates the current implementation of tunnel information modeling and its adoption in the infrastructure sector. The importance of structured and real-time data synchronization through technologies such as IoT and Big Data is emphasized. It explores advancements from tunnel model to shadow to DT, emphasizing the importance of structured and data real-time synchronization through technologies like IoT and Big Data. A comprehensive literature review highlights both technical and non-technical barriers to the implementation of DT. Continuous improvement of DT, supported by advancements in data acquisition and analytical methods, is expected to significantly enhance tunnel construction. As a main focus, this article provides a framework for a centralized and comprehensive platform for all levels of tunnel twin development, leveraging Autodesk Platform Services. It concludes with a vision for the future, discusses emerging technologies advocating for a strategic approach to digital transformation in tunneling that leverages technological innovations for sustainable development and societal benefits.

AI image generators based on diffusion models have recently garnered attention for their capability to create images from simple text prompts. However, for practical use in civil engineering they need to be able to create specific construction plans for given constraints. This paper investigates the potential of current AI generators in addressing such challenges, specifically for the creation of simple floor plans. We explain how the underlying diffusion-models work and propose novel refinement approaches to improve semantic encoding and generation quality. In several experiments we show that we can improve validity of generated floor plans from 6% to 90%. Based on these results we derive future research challenges considering building information modeling. With this we provide: (i) evaluation of current generative AIs; (ii) propose improved refinement approaches; (iii) evaluate them on various examples; (iv) derive future directions for diffusion models in civil engineering.

This article presents an innovative approach to structured information exchange in ground modeling, integrating geology, geotechnics, and hydrogeology. It proposes a data framework seamlessly merging the DAUB's modeling guidelines with the University of Innsbruck research. Addressing diverse data management challenges and ensuring interoperability, the workflow offers solutions for CAD- and interpolation-based modeling, enabling semi-automated generation of Geotechnical Synthesis Models with open file format export. Overcoming obstacles, the article introduces a uniform rasterization method, defining a tunnel-specific repository voxel (Tuxel). Results showcase a streamlined, semi-automated process using Autodesk Civil 3D® and Seequent Leapfrog Works®, enhancing data uniformity and enabling cross-project information exchange. This workflow provides practical solutions for collaborative ground modeling within BIM/TIM frameworks, fostering efficient and standardized data handling.

The understanding of the complex dynamic phenomena occurring during the reaming operations of the raise boring process is limited. Any operational damage of the reamer is very likely to be caused by component failure due to fatigue effects from the dynamic processes. Therefore, an innovative measurement setup consisting of acceleration sensors in three axes was installed on a reamer to capture its movements and vibrations during operation. The capture of the reaming process by acceleration sensors was successful, however no operational damage on the reamer was observed during the reaming of three vertical shafts. The collected vibration data was implemented into a dynamic finite element assessment, which is intended to serve for lifetime predictions of components and predictive maintenance. A further step is investigating the potential for the implementation of the measurement data into a condition monitoring system and for optimizing the reaming operation.