AbstractThe use of supporting fluids to stabilize excavations is a common technique adopted in the construction industry. Rapid detection of incipient collapse for deep excavations and timely ...decision making are crucial to ensure safety during construction. This paper explores a hybrid framework for forecasting the collapse of fluid-supported circular excavations by combining physics-based and data-driven modeling. Finite-element limit analysis is first used to develop a numerical database of stability numbers for both unsupported and fluid-supported circular excavations. The parameters considered in the modeling include excavation geometry, soil strength profile, and support fluid properties. A data-driven algorithm is used to learn the numerical results to develop a fast surrogate amenable for integration within real-time monitoring systems. By way of example, the proposed forecasting strategy is retrospectively applied to a recent field monitoring case history where the observational method is used to update the input parameters of the data-driven surrogate.
AbstractThe development of underground spaces inevitably poses significant risks to nearby infrastructure due to construction-induced ground displacements. While our understanding of tunnel-induced ...ground movements is now relatively mature, there is a distinct lack of literature on large-diameter open caisson shafts. This paper fills this gap by describing results from a small-scale laboratory study exploring soil deformation mechanisms during caisson construction in dry sand. Results from seven tests are analyzed to identify the influence of key caisson geometric properties as well as the effectiveness of external cofferdams in minimizing soil displacement. The results show that the primary mechanisms driving ground movements are a compressive ‘bearing’ front beneath the cutting face and a ‘frictional’ contribution above the cutting face. The normalized radial settlement profile is also shown to be insensitive to the normalized caisson embedment depth, and the settlement zone of influence extends up to 0.25 diameters below the caisson cutting edge. Furthermore, the presence of an external cofferdam is shown to be highly effective in reducing soil settlements. Quantitative analysis reveals a significant decrease in soil settlement with an increase in cofferdam depth from 0.25 to 0.5 of caisson depth, with good consistency between results for different soil elevations. In addition, larger cofferdam diameters provide maximum benefits in minimizing ground displacements.
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A multi-axis force sensor measures forces and moments occurring in more than one spatial direction. In this way, a single multi-axis sensor can perform what is essentially a ...three-dimensional measurement of physical quantities. This feature makes multi-axis sensors popular in a wide range of engineering research including automation, machining processes, aerospace, medical applications and civil engineering. Measurement of multi-directional forces and moments is typically achieved using multiple strain-sensitive elements mounted on an elastic structure. Both the sensitive elements and the elastic structure require careful consideration to design a force sensor for accuracy, reliability and robustness. While the development of multi-axis sensors has been considered extensively in the literature over the past seven decades, a collective resource which collates and examines this information does not exist. This review explores multi-axis force sensor developments across a broad range of disciplines. The salient fundamental sensing techniques adopted for the strain-sensitive elements reported in the literature are discussed and a critical review of elastic structure designs that have featured in the literature is also presented.
AbstractThis paper proposes a practical approach for data-efficient metamodeling and real-time modeling of laterally loaded monopiles using physics-informed multifidelity data fusion. The proposed ...approach fuses information from one-dimensional (1D) beam-column model analysis, three-dimensional (3D) finite element analysis, and field measurements (in order of increasing fidelity) for enhanced accuracy. It uses an interpretable scale factor–based data fusion architecture within a deep learning framework and incorporates physics-based constraints for robust predictions with limited data. The proposed approach is demonstrated for modeling monopile lateral load–displacement behavior using data from a real-world case study. Results show that the approach provides significantly more accurate predictions compared to a single-fidelity metamodel and a widely used multifidelity data fusion model. The model’s interpretability and data efficiency make it suitable for practical applications.
AbstractThe static stiffness of suction caisson foundations is an important engineering factor for offshore wind foundation design. However, existing simplified design models are mainly developed for ...nonlayered soil conditions, and their accuracy for layered soil conditions is uncertain. This creates a challenge for designing these foundations in offshore wind farm sites, where layered soil conditions are commonplace. To address this, this paper proposes a multifidelity data fusion approach that combines information from different physics-based models of varying accuracy, data sparsity, and computational costs in order to improve the accuracy of stiffness estimations for layered soil conditions. The results indicate that the proposed approach is more accurate than both the simplified design model and a single-fidelity machine learning model, even with limited training data. The proposed method offers a promising data-efficient solution for fast and robust stiffness estimations, which could lead to more cost-effective offshore foundation designs.
AbstractWith increasing demand for sustainable underground infrastructure and pressure to reduce embodied carbon (EC), microtunneling (MT) has become an increasingly popular trenchless method of ...installing buried utility tunnels. Life-cycle analyses have shown that trenchless methods cause lower emissions than traditional open-cut construction. However, existing literature specifically considering MT is limited and fails to consider the impact of the entire construction process. In this paper, an approach for calculating the EC of MT is presented. The proposed approach is applied to three recent case histories in the United Kingdom through the development of a bespoke MT EC database in collaboration with industry partners. Total emissions across all three projects (870 m of pipeline) total 1,005 tCO2e. Production of materials and components is shown to account for an average of 68.5% of EC across the three projects, with most of these emissions coming from the key structural materials, namely concrete and steel. Sensitivity analyses demonstrate that the source and production method of steel products have a significant impact on EC. Site activities also make a significant contribution, accounting for an average of 20.5% of total EC. Normalization of the results suggests that MT produces less EC than open-cut pipeline installation and highlighted how increasing drive lengths and reducing the number of shafts can significantly reduce EC. One of the case studies is then used as an example to quantify how the reduction of intermediate launch/reception shafts can reduce overall EC.
Practical ApplicationsIn this paper, an approach for calculating the embodied carbon (EC) of microtunneling (MT) is presented, defining a scope that will enable fair comparison of future projects. The presented methodology also provides a useful reference for readers to find sources for EC factors. The proposed approach is applied to three recent case histories in the United Kingdom through the development of a bespoke MT EC database in collaboration with industry partners. The presented results provide insights into the EC of MT projects in five key areas: (1) the relative contribution of different materials and different construction phases to overall EC, (2) the sensitivity of EC to the method of steel production, (3) the influence of on-site emissions, (4) the comparison of MT to traditional open-cut pipeline construction, and (5) quantifying the environmental benefits of minimizing the number of shafts.
AbstractThe proliferation of data collected by modern tunnel boring machines presents a substantial opportunity for the application of data-driven anomaly detection (AD) techniques that can adapt ...dynamically to site specific conditions. Based on jacking forces measured during microtunneling, this paper explores the potential for AD methods to provide a more accurate and robust detection of incipient faults. A selection of the most popular AD methods proposed in the literature, comprising both clustering- and regression-based techniques, are considered for this purpose. The relative merits of each approach is assessed through comparisons to three microtunneling case histories in which anomalous jacking force behavior was encountered. The results highlight an exciting potential for the use of anomaly detection techniques to reduce unplanned downtimes and operation costs.
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