Norwegian Geotechnical Institute (NGI) Digital Archive
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Sustainable impermeable landfill barriers: The potential of using geological waste and surplus masses
Get PDFThis study investigated the potential to reuse excavated cement stabilised clay (CSC) and press filter residual (PFR) as impermeable landfill barriers. First, laboratory experiments including particle size distribution, consistency limits, standard Proctor and permeability tests were carried out on reconstituted samples. Subsequently, a full-scale compaction trial was performed for both materials. Nuclear density tests and results from cylinder samples were compared to evaluate the compaction behaviour. The cylinder samples were utilised to quantify the properties of the compacted soil layers. Multi-sensor core logging (MSCL) and X-ray image techniques were adopted to visualise the homogeneity of the field samples. Results showed that both the CSC and PFR are well-graded and fine-grained soils which can be classified as high plastic silts or high plastic clays. For both materials, hydraulic conductivity values, k, less than 1x10-9 m/s were obtained when compacted in the laboratory to their maximum dry densities according to standard Proctor. The field compacted samples were, however, more permeable (e.g., k = 6.1x10-8 m/s (SD = 9.1x10-8, n = 3) for CSC and k = 1.5x10-9 m/s (SD = 4.3x10-10, n = 3) for PFR at a vertical stress, σv, of 40 kPa). Both materials reached the hydraulic conductivity requirements for barriers for inert waste landfills. For the PFR, an average k < 1x10-9 m/s was obtained at σv = 160 kPa which suggests that PFR might be reused as a bottom liner for ordinary and hazardous waste. The CSC results showed a considerable variability which can be explained by its innate heterogeneity.Sustainable impermeable landfill barriers: The potential of using geological waste and surplus massespublishedVersio
Characterization of particle size segregation and heterogeneity along the slopes of a waste rock pile using image analysis
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The Effect of Magma Poor and Magma Rich Rifted Margins on Continental Collision Dynamics
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Deep learning-based design model for suction caissons on clay
Get PDFPredicting the non-linear loading response is the key to the design of suction caissons. This paper presents a systematic study to explore the applicability of deep learning techniques in foundation design. Firstly, a series of three-dimensional finite element simulations was performed, covering a wide range of embedment ratios and different loading directions, to provide training data for the deep neural network (DNN) model. Then, hyper-parameter tuning was performed and it is found that the basic Fully-Connected (FC) neural network model is sufficient to capture the non-linear response of suction caissons with excellent accuracy and robustness. Furthermore, the optimized FC neural network model was also successfully applied to a database of suction caissons in sand, demonstrating its broad applicability. By comparing three typical DNNs, i.e., FC, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), it was observed that the FC neural network model excels over others in terms of simplicity, efficiency and accuracy. More importantly, by looking into the model’s generalization performance, the FC neural network model can also identify the change in foundation failure mechanisms. This study demonstrates the DNN’s powerful mapping ability and its potential for future use in offshore foundation design.Deep learning-based design model for suction caissons on claypublishedVersio
Shared mooring systems for offshore floating wind farms: A review
Get PDFOffshore wind energy, as a form of renewable power, has seen rapid development in recent years. While fixed-bottom wind turbines are typically used in water depths less than 50 m, the utilization of floating offshore wind turbines (FOWTs) becomes essential for deeper waters. Secure and effective mooring systems play a crucial role in making FOWTs commercially viable. The concept of a shared mooring system offers an innovative solution for deploying floating wind farms in clusters or arrays, which can reduce overall construction costs for large-scale floating wind farms. It is imperative to optimize the shared mooring arrangement for maximum cost-effectiveness and wind farm stability. However, implementing a shared mooring system introduces complexity to the dynamics of FOWTs, requiring the development of advanced simulation tools to meet modelling requirements. Under the shared mooring arrangement, mooring lines and anchors face more significant challenges, such as chain-seabed interactions, soil cyclic weakening, and anchor out-of-plane loading, which underscore the need for innovative, reliable, and efficient shared anchor designs. This article offers an overview of the current research status on shared mooring systems for floating wind farms, which might serve as a valuable reference for the construction of large-scale floating wind farms worldwide.Shared mooring systems for offshore floating wind farms: A reviewpublishedVersionpublishedVersio
Potential applications of deep learning in automatic rock joint trace mapping in a rock mass
Get PDFIn blasted rock slopes and underground openings, rock joints are visible in different forms. Rock joints are often exposed as planes confining rock blocks and visible as traces on a well-blasted, smooth rock mass surface. A realistic rock joint model should include both visual forms of joints in a rock mass: i.e., both joint traces and joint planes. Imaged-based 2D semantic segmentation using deep learning via the Convolutional Neural Network (CNN) has shown promising results in extracting joint traces in a rock mass. In 3D analysis, research studies using deep learning have demonstrated outperforming results in automatically extracting joint planes from an unstructured 3D point cloud compared to state-of-the-art methods. We discuss a pilot study using 3D true colour point cloud and their source and derived 2D images in this paper. In the study, we aim to implement and compare various CNN-based networks found in the literature for automatic extraction of joint traces from laser scanning and photogrammetry data. Extracted joint traces can then be clustered and connected to potential joint planes as joint objects in a discrete joint model. This can contribute to a more accurate estimation of rock joint persistence. The goal of the study is to compare the efficiency and accuracy between using 2D images and 3D point cloud as input data. Data are collected from two infrastructure projects with blasted rock slopes and tunnels in Norway.Potential applications of deep learning in automatic rock joint trace mapping in a rock masspublishedVersio
A look at the blind Kumamoto experiment: combining active and passive seismic observations to avoid Rayleigh-wave mode misidentification
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