Search

The convergence-confinement method (CCM) is a method that has been introduced in tunnel construction that considers the ground response to the advancing tunnel face and the interaction with installed support. One limitation of the CCM is due to the numerically or empirically driven nature of the longitudinal displacement profile and the incomplete consideration of the longitudinal arching effect that occurs during tunnelling operations as part of the face effect. In this paper, the authors address the issue associated with when the CCM is used within squeezing ground conditions at depth. Based on numerical analysis, the authors have proposed a methodology and solution to improving the CCM in order to allow for more accurate results for squeezing ground conditions for three different excavation cases involving various excavation-support increments and distances from the face to the supported front. The tunnelling methods of consideration include: tunnel boring machine, mechanical (conventional), and drill and blast. [ABSTRACT FROM AUTHOR]

In addressing limitations of conventional ground support measurement, a novel optical strain sensing technique is presented that is capable of measuring strain at increments as low as 0.65 mm along the entirety of an optical sensor affixed to a support element. The technique considers monitoring three sensing lengths along the profile of a fully grouted rock bolt element using a single optical fiber, which, in turn, allows the derivation of both the principle strain and principle strain direction along the bolt. A series of three experiments, which include: symmetric bending, combined axial load and bending, and double shear loading are presented and highlight the potential of the technique to capture bolt behaviour under such generalized rock bolt loading conditions. Analogized as a distributed strain rosette, the optical technique can distinguish both the coaxial and bending induced constituents of the total strain in the bolt regardless of load orientation. [ABSTRACT FROM AUTHOR]

Methods of approximation that predict the mechanical responses of the tunnel support systems in conjunction with ground behaviour are invaluable to the tunnel design engineer. Analytical models are often used in order to predict and/or validate ground-support behaviour. Conventionally, these analytical models do not account for the complex loading and reacting conditions of umbrella arch support systems throughout the tunnel excavation and support sequence. As such, a semi-analytical model is proposed within this paper for umbrella-arch systems that employ an umbrella ache with forepoles, in squeezing-ground conditions. The semi-analytical model is based on an assortment of applicable methods and theories depending on the relevant loading. Beam theory, elastic foundation theory, and the Convergence-Confinement Method (CCM) are all incorporated within the proposed analytical method. After a review of the literature it became apparent that a limited amount of models existed for squeezing-ground conditions. Previous models were based on gravity-driven (Silo Theory) loading conditions rather than the more applicable stress-driven (squeezing) loading conditions. The results of the semi-analytical approach included herein were able to reasonably capture the displacement profiles associated with captured field data. This semi-analytical approach can be considered for use by tunnel design engineers in order to aid them with tunnel support design. [ABSTRACT FROM AUTHOR]

Rockfall constitutes a major threat to the safety and sustainability of transport corridors bordered by rocky cliffs. This research introduces a new approach to rockfall susceptibility modeling for the identification of potential rockfall source zones. This is achieved by developing a data-driven model to assess the local slope morphological attributes with respect to the rock slope evolution processes. The ability to address "where" a rockfall is more likely to occur via the analysis of historical event inventories with respect to terrain attributes and to define the probability of a given area producing a rockfall is a critical advance toward effective transport corridor management. The availability of high-quality digital volumetric change detection products permits new developments in rockfall assessment and prediction. We explore the potential of simulating the conceptualization of slope-scale rockfall susceptibility modeling using computer power and artificial intelligence (AI). We employ advanced 3D computer vision algorithms for analyzing point clouds to interpret high-resolution digital observations capturing the rock slope evolution via long-term, LiDAR-based 3D differencing. The approach has been developed and tested on data from three rock slopes: two in Canada and one in the UK. The results indicate clear potential for AI advances to develop local susceptibility indicators from local geometry and learning from recent rockfall activity. The resultant models produce slope-wide rockfall susceptibility maps in high resolution, producing up to 75% agreement with validated occurrences. [ABSTRACT FROM AUTHOR]

• Confirming in-situ Structural Health Monitoring instruments are in working order. • Determine the in-situ properties of timber in real service conditions. • 3D, bimodular, orthotropic numerical model of a timber Warren truss structure. • Local in-situ load testing of a full-scale, heritage timber structure. • Identification of load and stress reversal within select timber members. This research paper presents the experimental results of an in-situ load-testing program within a WWII era, timber Warren truss structure located at Canadian Forces Base St-Jean, Quebec. Four point load tests were carried out on a 68 m long, Douglas fir timber truss instrumented with conventional electric strain gauges, distributed fibre optic sensors, and string potentiometers, among other instruments. These instruments makeup a larger, long-term structural health monitoring program implemented within the truss structure. The objectives of the local point load tests were to (1) confirm the in-situ monitoring instruments are in working order, (2) compare calculated mechanical properties with assumed properties, and (3) calibrate a numerical model capable of replicating truss response and compare to current modeling standards used within the Department of National Defence. Non-destructive testing, including moisture content and cable tension tests, was also conducted to determine existing material properties. Load testing and non-destructive testing results identified variations between the existing properties of the truss and the assumed properties of certain structural members, as well as identifying possible lower strength or stiffness values of stress-reversed members. Results also indicated the importance of analyzing timber as an orthotropic, bimodular material for reliability and conservatism, particularly in numerical modeling. Lastly, the results presented in this paper outline the importance of in-situ load testing with an aim to determine existing material properties for monitoring projects. Moreover, the identification of the existing properties and behaviour of the structure is fundamental in the development of a structural health monitoring program for emergency purposes. [ABSTRACT FROM AUTHOR]

The article presents a response to the discussions made by Robert K. Barrett and Dov Leshchinsky on the authors' paper "The influence of facing stiffness on the performance of two geosynthetic reinforced soil retaining walls." They appreciate the comments raised by Barrett and Leshchinsky. In doing so, they clarify and elucidate a number of points related to the details and contingents of the test program described in the referring paper. Moreover, they provide various implications to current practice.

Current limit equilibrium-based design methods for the internal stability design of geosynthetic reinforced soil walls in North America are based on the American Association of State Highway and Transportation Officials (AASHTO) Simplified Method. A deficiency of this approach is that the influence of the facing type on reinforcement loads is not considered. This paper reports the results of two instrumented full-scale walls constructed in a large test facility at the Royal Military College of Canada. The walls were nominally identical except one wall was constructed with a stiff face and the other with a flexible wrapped face. The peak reinforcement loads in the flexible wall were about three and a half times greater than the stiff-face wall at the end of construction and about two times greater at the end of surcharging. The stiff-face wall analysis using the Simplified Method gave a maximum reinforcement load value that was one and a half times greater than the measured value at the end of construction. Furthermore, the surcharge pressure required to reach the creep-limited strength of the reinforcement was about two times greater than the predicted value. The results demonstrate quantitatively that a stiff facing in a reinforced soil wall is a structural component that can lead to significant reductions in reinforcement loads compared to flexible facing systems. [ABSTRACT FROM AUTHOR]

A high spatial resolution distributed optical fiber strain sensing technology is demonstrated to advance the assessment of support element behaviour during in-situ pull tests. A technique to instrument typical tendon support elements with a fiber optic sensor is discussed and was trialed at an underground salt mine through a series of pull tests on instrumented rebar elements. It is shown that a continuous strain profile can be measured along the length of a support element under pull test load, which, in turn, allows the interfacial shear stress distribution and deformation of the support element to be resolved. In comparison to load and displacement measurements solely at the support element head, which is what is traditionally reported for in-situ pull tests, the optical fiber strain sensor is found to provide significantly more insight into the mechanistic behaviour of the support element by readily measuring highly variable strain distributions and by quantifying the load development length of the support element. The sensor arrangement allowed the conventional handling and installation procedures to be followed without any implication to the integrity of the sensor. Accordingly, an instrumented reinforcement element can be installed with minimal interruption to on-going mining/construction procedures at the given project. • Distributed optical fiber strain sensing is used to augment the in-situ pull test. • A fiber optic sensor instrumenting procedure is discussed. • Instrumented support elements are installed and pull tested in an underground mine. • Coaxial strain distributions are measured along rebar rock bolts in-situ. • Support element bond behaviour is quantified and compared using various sensors. [ABSTRACT FROM AUTHOR]

Reality capture technologies such as Structure-from-Motion (SfM) photogrammetry have become a state-of-the-art practice within landslide research workflows in recent years. Such technology has been predominantly utilized to provide detailed digital products in landslide assessment where often, for thorough mapping, significant accessibility restrictions must be overcome. UAV photogrammetry produces a set of multi-dimensional digital models to support landslide management, including orthomosaic, digital surface model (DSM), and 3D point cloud. At the same time, the recognition of objects depicted in images has become increasingly possible with the development of various methodologies. Among those, Geographic Object-Based Image Analysis (GEOBIA) has been established as a new paradigm in the geospatial data domain and has also recently found applications in landslide research. However, most of the landslide-related GEOBIA applications focus on large scales based on satellite imagery. In this work, we examine the potential of different UAV photogrammetry product combinations to be used as inputs to image segmentation techniques for the automated extraction of landslide elements at site-specific scales. Image segmentation is the core process within GEOBIA workflows. The objective of this work is to investigate the incorporation of fully 3D data into GEOBIA workflows for the delineation of landslide elements that are often challenging to be identified within typical rasterized models due to the steepness of the terrain. Here, we apply a common unsupervised image segmentation pipeline to 3D grids based on the superpixel/supervoxel and graph cut algorithms. The products of UAV photogrammetry for two landslide cases in Greece are combined and used as 2D (orthomosaic), 2.5D (orthomosaic + DSM), and 3D (point cloud) terrain representations in this research. We provide a detailed quantitative comparative analysis of the different models based on expert-based annotations of the landscapes and conclude that using fully 3D terrain representations as inputs to segmentation algorithms provides consistently better landslide segments. [ABSTRACT FROM AUTHOR]

A support system of weak rock tunnels typically consists of two components, namely an artificial support structure of combined categories and a rock mass surrounding the tunnel. Stability analysis and design methods of the artificial support structure have reached a mature developmental stage, whereas that of the surrounding rock has few progresses, especially when considering the surrounding rock as one of the support components. For this, from the perspective of considering the surrounding rock as one of the support components, an analytical method is proposed to calculate the factor of safety of the surrounding rock component, along with a stability analysis method for the complete tunnel support system (i.e. surrounding rock combined with artificial supports). This analytical method is based on an evaluation approach developed to obtain the stable ultimate deformation of the tunnel's surrounding rock, along with the minimum internal pressure to stabilize the tunnel. The evaluation approach is on the basis of the generalized Hoek–Brown yield criterion that is considered as the non-linear behaviour of the rock mass, combined with Rabcewicz's mode of initial instability as well as Carranza–Torres's solution to tunnel deformation. The validity of the developed evaluation approach is verified by comparing the results to numerical simulation outputs and field data. A comprehensive parametric analysis is also conducted to investigate the effects of parameters involved in the evaluation approach on the stable ultimate deformation and minimum internal pressure. Subsequently, a suggestion is made aiming to revise the notion of 'optimal installation timing' of artificial supports for classical support design methods. This research forms a complete set of quantitative analysis tools for the stability and design of the support system of underground tunnels excavated in weak rocks. [ABSTRACT FROM AUTHOR]

Rockfall risk management has traditionally been built upon scenarios based on statistical analysis of historic frequency-magnitude rockfall patterns which allows for an estimation of the return period of rockfalls within given ranges of magnitude. Τhe availability of rockfall databases is thereby of crucial importance to quantify rockfall activity. In recent years, engineering geologists have been able to leverage light detection and ranging (LiDAR) technology with automated change detection and clustering workflows, for spatially accurate rockfall monitoring and inventorization. This wealth of digital data generates new research prospects for the development of an artificial intelligence (AI) system able to analyze change detection sequences and map spatio-temporal rockfall patterns by precisely capturing the rock slope evolution in near-real-time. However, prerequisite to such an advancement is the need to efficiently automate the process of classifying surface changes corresponding to rockfall. This task typically requires intensive human effort and is prone to multiple sources of human error like subjectivity, expertise, and experience that may differ from person to person involved in a particular project. This paper investigates the potential of integrating sophisticated deep learning architectures into dynamic rockfall database population processes, with the goal of relieving experts from the daunting task of manually classifying rockfall events within large loads of change detection data. Deep neural networks based on the pioneering PointNet and PointNet++ architectures for 3D point cloud learning are developed for this purpose based on a 5-year change detection database consisting of >8000 rock slope clusters of identified change for training, with scanning intervals ranging from 5 days to 6 months. The models are tested on the 536 clusters from the two last data acquisitions to simulate the real monitoring situation and subsequently on the most frequent of the campaigns to increase the probability of working with single-event clusters. The best-performing model achieves an accuracy of about 89% and 84% on the last and shortest campaign, respectively. The optimized deep learning models are further evaluated on a geologically different rockfall database achieving almost 93% accuracy in a location where discrete geomorphologic features such as steep rock outcrops and erosion channels are present. The study shows that although it is challenging to achieve generalization in rockfall detection, site-specific training of the proposed deep learning architecture can lead to high-level performance and support further advancements in rockfall risk management. • LiDAR monitoring provides detailed 3D point cloud models of rock slope deformations. • High-level feature learning from point clouds is achieved with modern deep learning. • Rockfall detection can be approached as a binary point cloud classification problem. • Global shape feature learning is suggested compared to local texture features. • The prediction confidence increases with the cluster volume. [ABSTRACT FROM AUTHOR]

Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spatial representations of our physical world, known as 3D point clouds, that are mainly used for visualization and monitoring purposes. To extract knowledge from point clouds and inform decision-making within rock slope management systems, semantic injection through automated processes is necessary. In this paper, we propose a model that utilizes a segmentation procedure which delivers segments ready to classify and be retained or rejected according to complementary knowledge-based filter criteria. First, we provide relevant voxel-based features based on the local dimensionality, orientation, and topology and partition them in an assembly of homogenous segments. Subsequently, we build a decision tree that utilizes geometrical, topological, and contextual information and enables the classification of a multi-hazard railway rock slope section in British Columbia, Canada into classes involved in landslide risk management. Finally, the approach is compared to machine learning integrating recent featuring strategies for rock slope classification with limited training data (which is usually the case). This alternative to machine learning semantic segmentation approaches reduces substantially the model size and complexity and provides an adaptable framework for tailored decision-making systems leveraging rock slope semantics. [ABSTRACT FROM AUTHOR]