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Traditional analysis of embedded earth-retaining walls relies on simplistic lateral earth pressure theory methods, which do not allow for direct computation of wall displacements. Contemporary numerical models rely on the Mohr–Coulomb model, which generally falls short of accurate wall displacement prediction. The advanced constitutive small-strain hardening soil model (SS-HSM), effectively captures complex nonlinear soil behavior. However, its application is currently limited, as SS-HSM requires multiple input parameters, rendering numerical modeling a challenging and time-consuming task. This study presents an extensive numerical investigation, where wall displacements from numerical models are compared to empirical findings from a large and reliable database. A novel automated computational scheme is created for model generation and advanced data analysis is undertaken for this objective. The main findings indicate that the SS-HSM can provide realistic predictions of wall displacements. Ultimately, a range of input parameters for the utilization of SS-HSM in earth-retaining wall analysis is established, providing a good starting point for engineers and researchers seeking to model more complex scenarios of embedded walls with the SS-HSM. [ABSTRACT FROM AUTHOR]

Over the past decades, numerical modelling has become a powerful tool for rock mechanics applications. However, the accurate estimation of rock mass input parameters remains a significant challenge. Machine learning (ML) tools have recently been integrated to enhance and accelerate numerical modelling processes. In this paper, we demonstrate the novel use of ML tools for calibrating a state-of-the-art three-dimensional (3D) finite-element (FE) model of a kinematic structurally controlled failure event in an open-pit mine. The failure event involves the detachment of a large wedge, thus allowing for the accurate identification of the geometry of the rock joints. FE models are automatically generated according to estimated ranges of joint input parameters. Subsequently, ML tools are used to analyze the synthetic data and calibrate the strength parameters of the rock joints. Our findings reveal that a relatively small number of models are needed for this purpose, rendering ML a highly useful tool even for computationally demanding FE models. [ABSTRACT FROM AUTHOR]

In their highly influential paper, Yarkoni, Tal, and Jacob Westfall. 2017. "Choosing Prediction over Explanation in Psychology: Lessons from Machine Learning." Perspectives on Psychological Science 12 (6):1100–1122. the authors highlight difficulties in traditional explanatory research in the field of psychology and argue in favour of novel data-driven science. By applying machine-learning methods to large data sets, predictive power has been shown to increase significantly. Geological engineers are responsible for a wide range of applications, including the design of tunnels, dams, foundations, and mines. While the field of geological engineering stands on solid mechanistic grounds, we argue that its predictive aspect aligns more closely with psychology than other mechanistic sciences. We therefore propose a paradigm shift in geological engineering research towards a prediction-centric approach. Potentially, this could enhance cost-effectiveness in structural design and lead to substantial societal savings. [ABSTRACT FROM AUTHOR]

In observational method projects in geotechnical engineering, the final geotechnical design is decided upon during actual construction, depending on the observed behavior of the ground. Hence, engineers must be prepared to make crucial decisions promptly, with few available guidelines. In this paper, we propose coupling numerical analysis with machine learning (ML) algorithms for enhancing the decision process in observational method projects. The proposed methodology consists of two main computational steps: (1) data generation, where multiple numerical models are automatically generated according to the anticipated range of input parameters, and (2) data analysis, where input parameters and model results are analyzed with ML models. Using the case study of the Semel tunnel in Tel Aviv, Israel, we demonstrate how this computational process can contribute to the success of observational method projects through (1) the computation of feature importance, which can assist with better identifying the key features that drive failure prior to project execution, (2) providing insights regarding the monitoring plan, as correlative relationships between various results can be tested, and (3) instantaneous predictions during construction. [ABSTRACT FROM AUTHOR]

The potential of machine learning (ML) tools for enhancing geotechnical analysis has been recognized by several researchers. However, obtaining a sufficiently large digital dataset is a major technical challenge. This paper investigates the use of transfer learning, a powerful ML technique, used for overcoming dataset size limitations. The study examines two scenarios where transfer learning is applied to tunnel support analysis. The first scenario investigates transferring knowledge between a ground formation that has been well-studied to a new formation with very limited data. The second scenario is intended to investigate whether transferring knowledge is possible from a dataset that relies on simplified tunnel support analysis to a more complex and realistic analysis. The technical process for transfer learning involves training an Artificial Neural Network (ANN) on a large dataset and adding an extra layer to the model. The added layer is then trained on smaller datasets to fine-tune the model. The study demonstrates the effectiveness of transfer learning for both scenarios. On this basis, it is argued that, with further development and refinement, transfer learning could become a valuable tool for ML-related geotechnical applications. [ABSTRACT FROM AUTHOR]

Numerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) is a critical input for many rock engineering problems. However, no available method allows the quantification of GSI input parameters, and engineers must consider a range of values. As projects progress, these ranges can be narrowed down. Machine learning (ML) algorithms have been coupled with numerical modeling to create surrogate models. The concept of surrogate models aligns well with the deductive nature of data availability in rock engineering projects. In this paper, we demonstrated the use of surrogate models to analyze two common rock slope stability problems: (1) determining the maximum stable depth of a vertical excavation and (2) determining the allowable angle of a slope with a fixed height. Compared with support vector machines and K-nearest algorithms, the random forest model performs best on a data set of 800 numerical models for the problems discussed in the paper. For all these models, regression-type models outperform classification models. Once the surrogate model is confirmed to preform accurately, instantaneous predictions of maximum excavation depth and slope angle can be achieved according to any range of input parameters. This capability is used to investigate the impact of narrowing GSI range estimation. [ABSTRACT FROM AUTHOR]

Smuggling and warfare tunnels are unique structures that have rarely been studied from an engineering perspective. A notable example is the vast networks of tunnels that were secretly constructed underneath the Gaza Strip. Particularly because these tunnels were not designed and constructed via traditional engineering practice, they constitute an interesting case study. The tunnels are supported by thin precast concrete elements, with the wall elements being the critical structural element. While some instances of structural failure and collapse have been reported in the media, a great number of the tunnels have remained stable. In this paper, we attempt to conduct a forward analysis to estimate the load and response of the wall elements. We estimate the range of problem input parameters based on multiple sources, including media accounts, geological research papers, and geotechnical reports obtained from the vicinity of the Gaza tunnels. The problem is then analyzed using two approaches: (1) a simplified structural analysis based on lateral earth-pressure theory and (2) numerical modeling. Both analysis methods show that the wall elements should fail due to compression even under the most favorable estimates of input parameters, in contrast to actual reality. We discuss possible explanations for this disparity. While it is not possible to pinpoint the exact explanation, we argue that current geotechnical practice is generally biased toward conservatism, even prior to the application of safety factors. [ABSTRACT FROM AUTHOR]

This paper presents a philosophical examination of classical rock engineering problems as the basis to move from traditional knowledge to radical (innovative) knowledge. While this paper may appear abstract to engineers and geoscientists more accustomed to case studies and practical design methods, the aim is to demonstrate how the analysis of what constitutes engineering knowledge (what rock engineers know and how they know it) should always precede the integration of new technologies into empirical disciplines such as rock engineering. We propose a new conceptual model of engineering knowledge that combines experience (practical knowledge) and a priori knowledge (knowledge that is not based on experience). Our arguments are not a critique of actual engineering systems, but rather a critique of the (subjective) reasons that are invoked when using those systems, or to defend conclusions achieved using those systems. Our analysis identifies that rock engineering knowledge is shaped by cognitive biases, which over the years have created a sort of dogmatic barrier to innovation. It therefore becomes vital to initiate a discussion on the subject of engineering knowledge that can explain the challenges we face in rock engineering design at a time when digitalisation includes the introduction of machine algorithms that are supposed to learn from conditions of limited information. [ABSTRACT FROM AUTHOR]

Despite recent efforts, digitization in rock engineering still suffers from the difficulty in standardizing and statistically analysing databases that are created by a process of quantification of qualitative assessments. Indeed, neither digitization nor digitalization have to date been used to drive changes to the principles upon which, for example, the geotechnical data-collection process is founded, some of which have not changed in several decades. There is an empirical knowledge gap that cannot be bridged by the use of technology alone. In this context, this paper presents the results of what the authors call a rediscovery of rock mass classification systems, and a critical review of their definitions and limitations in helping engineers to integrate these methods and digital acquisition systems. This discussion has significant implications for the use of technology as a tool to directly determine rock mass classification ratings and for the application of machine learning to address rock engineering problems. Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology [ABSTRACT FROM AUTHOR]

The topic of Building Information Modelling (BIM) adoption by public organizations has become a central subject of research, and a significant amount of BIM documents, guidelines, and standards have been created to meet different organizational purposes. Compared to the building industry, the application of BIM tools for tunnel project management is lagging far behind. This paper proposes a methodology for integrating BIM tools for conventional tunnelling. A fundamental distinction is made between the tunnel internal architectural domain and the external structural domain. For the former, BIM methodology can be applied similarly to the building industry. For the latter, it is suggested that a BIM model be built according to the essential information generated during tunnelling excavation. The proposed methodology was put to test for an actual tunneling project. A routine was established where the supervisor on behalf of the owner was responsible for gathering and reporting essential data in tabular form. Via REVIT's Application Programming Interface (API), a code was developed so that a BIM model was built and updated automatic to data insertion. Ultimately, the final BIM model allows managing up-to-date qualitative and quantitative information visually. Thus, human understanding and interpretation are enhanced for future uses, such as maintenance, future renovations and project post-analysis. [ABSTRACT FROM AUTHOR]

• Fiber reinforced concrete slabs were tested under close-in and contact detonations. • The damage and response modes were recorded. • Fiber reinforcement was found to be effective only for the contact detonations. • Close-in detonations were manifested by a global response. • Available empirical tools need to be improved to consider the concrete mix parameters. This paper presents an experimental research to study the behavior of fiber-reinforced concrete slabs with varying fiber volume and type, subjected to contact and close-in detonations. A total of 25 full-scale blast tests were carried out. The results are analyzed, discussed and compared to fast-running tools. For the close-in detonations, the damage was manifested by a global flexural response and the fiber volume did not affect slab performance. For the contact blasts, localized damage in the forms of cratering, spalling, and in some cases perforation, was observed. For these tests, increasing fiber volume was found to improve slab performance. [ABSTRACT FROM AUTHOR]