In this study, 2D and 3D modelling strategies are used to represent the behaviour of historical masonry buildings on strip foundations undergoing settlements. The application focuses on a two-story building, typical of the Dutch architectural heritage. An improved 2D modelling is presented: It includes the effect of the lateral walls to replicate the response of the detailed 3D models. The masonry strip foundation is modelled and supported by a no-tension interface, which represents the soil-foundation interaction. Two settlement configurations, hogging and sagging, are applied to the models, and their intensity is characterized using their angular distortion. The improved 2D model that includes the stiffness and weight of the lateral walls agrees in terms of displacements, stress and damage with the detailed 3D models. Conversely, the simplified 2D facade models without lateral walls exhibit different cracking, and damage from 2 to 7 times lower at an applied angular distortion of 2 parts per thousand (1/500). The improved 2D model requires less computational and modelling burden, resulting in analyses from 9 to 40 times faster than the 3D models. The results prove the importance of identifying and including the 3D effects that affect the response of structures subjected to settlements.

Time and again earthquake-induced damage occurs worldwide as a result of soil liquefaction, especially in loose sands with high groundwater levels. One of the most common types of damage caused by liquefaction results from vertical soil deformations, in other words settlements. This article contributes to the determination of earthquake-related settlements in dry and saturated sands using two-dimensional finite element analyses (2D-FEA) and semi-empirical methods. The comparison of the results obtained from both methods showed that the 2D-FEA give relatively low settlement values compared to the values obtained from the semi-empirical methods. This is mainly explained by the relatively large horizontal earthquake accelerations resulting from the one-dimensional, equivalent-linear dynamic analyses and by the accumulation of earthquake waves at the upper edges of the numerical models.

The stress state of soil may affect the building settlements induced by tunnelling, which, however, has not been well understood. In this study, three dimensional numerical analyses combined with in situ measurements were performed to investigate the geostress-associated settlements of a raft-foundation building due to tunnelling in soft ground. Basically, two types of geostress fields were investigated: the first type considered the effect of additional stress generated in the foundation soil (FAS) due to building weight, while in the second type, a sequential twin tunnelling was presumed, and the effect of additional soil stress induced by the first tunnel (TAS) on the building response to the second tunnel was considered. The results indicated that FAS may aggravate the stress release of the foundation soil, and thus gave rise to a larger building settlement or inclination. In the sequential tunnelling process, the effect of TAS can be more complex: when the first tunnel lowered the stress of foundation soil, TAS effect of the first tunnel may help reduce the building settlements induced by the second tunnel; otherwise, it may aggravate building settlements. In addition to TAS effect, the sheltering effect was also found to play an important part in twin tunnelling.

This study compares two 3D nonlinear FE models, 'simplified coupled' and 'uncoupled', to explore 'light' damage in a two-storey masonry building on strip foundations affected by subsidence. Both models employ nonlinear interfaces to simulate soil-structure interaction: the simplified coupled model ties the structure with the soil volume with 'contact interfaces', while the uncoupled model uses 'boundary interfaces' to represent the interaction. The impact of soil volume and settlement shape size is examined. Results indicate consistent damage, displacements, and stresses across both modelling approaches with the smallest soil volume. Differences increase with larger soil volumes: at a distortion of 1/1,000 in hogging, the coupled model shows the damage decreases by 54% when the soil volume is quadrupled. Mesh size is also observed to affect crack initiation but not the overall damage mechanism. In general, coupled models reduce non-convergence and computation time, whereas uncoupled models simplify the analyses by decoupling the problem.

Assessing the potential damage to unreinforced masonry (URM) buildings under soil subsidence is a complex task, due to several factors associated with URM mechanical behaviour and soil-structure interaction. The remarkable variability in material properties of masonry may be further exacerbated by degradation processes, with repercussions on the overall structural response. Furthermore, both in-situ surveys and laboratory tests point out a major role being played by bond pattern effects and strength ratios between masonry constituents on crack formation, distribution and progression. Advanced numerical methods such as those based on masonry micro-modelling might be employed to realistically account for such factors, explicitly incorporating material discontinuities, fragmentation, and collision. In this paper, the Applied Element Method (AEM) is used to simulate the nonlinear structural response and damage of two tuff stone masonry walls with opening, which were experimentally tested under soil settlement in intact and deteriorated conditions. A satisfactory numerical-experimental agreement is found, allowing damage propagation phenomena as well as load redistributions between structural elements to be captured. Such results can then be used as a basis to perform further investigation considering more complex scenarios at structural scale.

Assessment of tunnelling-induced building damage is a complex Soil-Structure Interaction (SSI) probelm, influenced by numerous geometric and material parameters of both the soil and structures, and is characterised by strong non-linear behaviour. Currently, there is a trend towards developing data-driven models using Machine Learning (ML) to capture this complex behaviour. Given the scarcity of real data, which typically comes from specific case studies, many researchers have turned to creating extensive synthetic datasets via sophisticated and validated numerical models like Finite Element Method (FEM). However, the development of these datasets and the training of advanced ML algorithms present significant challenges. poses significant challenges. Reliance solely on parameter domains and ranges derived from case studies can lead to imbalanced data distributions and subsequently poor performance of models in less populated regions. In this paper, we introduce a strategy for designing optimal high-confidence datasets through an iterative procedure. This process begins with a systematic literature review to determine the importance of parameters, their ranges, and dependencies as they pertain to building damage induced by SSI. Starting with several hundred FEM simulations, we generate an initial dataset and assess its quality and impact through Sensitivity Analysis (SA) studies, statistical modelling, and re-sampling in statistically significant regions. This evaluation allows us to refine the model's input space, seeking scenarios that mitigate output distribution imbalances. The procedure is repeated until the datasets achieve a satisfactory balance for training metamodels, minimising bias effectively. Our findings highlight the success of this approach in identifying an optimal and feasible input space that significantly reduces imbalanced distributions of output features. This approach not only proves effective in our study but also offers a versatile methodology that could be adapted to other disciplines aiming to generate high-quality synthetic datasets.

This paper presents two case studies dealing with undesirable impacts of overburden drilling of casings for end-bearing piles to bedrock. Monitored pore-water pressures and ground settlements are used to document and assess the influence from rotary percussive drilling with down-the-hole (DTH) hammers. The studies show that drilling with high-pressure air -driven DTH hammers may cause considerable erosion and soil volume loss adjacent to the drill bit and along the casing, resulting in settlements of the surrounding ground. The risk of soil volume loss increases when the drilling is carried out in erodible soils such as silt and fine sands. The volume loss is found to be caused by a combined air -lift pump effect and a Venturi suction effect. Monitoring pore pressures in the vicinity of the drilling may be used to reduce soil volume loss and prevent damaging settlements. Results from drilling with water-driven DTH hammer showed significantly less ground settlements and influence on pore pressures compared to using an air -driven hammer. The study suggests that the drilling parameters flow rate and penetration rate, and the cross-sectional area of the pile casing can be combined in a non-dimensional methodology to assess the mass balance of drill cuttings when drilling with water flushing. A design framework is suggested to guide overburden drilling in urban settings to reduce potential impact on the surroundings.

Engineers often estimate the amount of liquefaction-induced building settlements (LIBS) as a performance proxy to assess the potential of earthquake-induced damage to buildings. The first robust LIBS models were initially developed in 2017 and 2018 using traditional statistical approaches. More recently, machine learning techniques have started to be used in developing LIBS models. These recent efforts are a step forward in realizing the potential of machine learning in liquefaction engineering; however, they have often considered only one ML technique for a given dataset and typically used only held-out test sets for model assessment. In this study, five ML-based LIBS models with varying flexibility (i.e., ridge regression, partial least square regression - PLSR, random forest, gradient boosting decision tree - GBDT, and support vector regression) are developed using a LIBS database generated by soil-structure numerical simulations of different buildings and soil profiles shaken by ground motions with varying intensity measures. The motivation for considering models with different flexibility is to include different bias-variance trade-offs. Feature selection with different ML techniques indicates that cumulative absolute velocity, spectral acceleration at one second, contact pressure, foundation width, the thickness of the liquefiable layer, and a shearing liquefaction index are important features in estimating LIBS. The developed ML-based models are assessed considering prediction accuracy in test sets, performance against centrifuge tests and case histories, and trends. The assessment indicates that the random forest, GBDT, and SVR models perform best, providing standard deviation reductions up to 40% relative to a multi-linear regression. Specifically, the random forest and GBDT models exhibit a root mean square error (RMSE) of 0.29 and a coefficient of determination (R2) of 0.93 on test sets, demonstrating a notable improvement compared to a traditional multi-linear regression model, which yields an RMSE of 0.47 and an R2 of 0.82. Moreover, random forest and GBDT, alongside SVR, show a good performance in centrifuge tests and case histories. Finally, given the scarcity of LIBS models, this study also contributes to treating epistemic uncertainties in estimating LIBS, which is ultimately beneficial for performance-based assessments.

Leakage into underground constructions can result in time-dependent settlements in soft clays. In urban areas with spatial variability in geologic stratification, groundwater conditions and soil compressibility, differential settlements may occur, causing damage to buildings. Current methods for damage assessment that rely on 1D formulations for settlement prediction are not representative for drawdown-induced settlements in heterogeneous environments. Thus, in this paper, we propose a stand-alone approach to integrating spatially distributed, non-Gaussian settlement data into early-stage building damage assessments at a district scale. Deformations computed using a 2D coupled hydro-mechanical finite element model with an advanced constitutive model were then employed to get the time-dependent settlements computed as a 3D grid (along x- and y-directions) over a large area. Building damage was then calculated from these green-field simulations with typically used damage parameters for each building-specific settlement profile and comparing these with damage criteria. The approach was applied to 215 buildings in central Gothenburg, Sweden by simulating scenarios of 10 kPa and 40 kPa pore pressure drawdown in the lower (confined) aquifer. Several scenarios were studied, and the correlation between damage parameters and damage criteria was assessed. Finally, a sensitivity study on grid resolution was performed, as well as a validation against observed damage data. The proposed methodology offers an effective way for early-stage damage assessments at a large area for non-Gaussian settlements so that further investigations and mitigation measures can be targeted to the buildings and locations at the highest risk for damage.

Loess soil presents a collapsible behavior and suffers significant settlements due to wetting that can seriously affect the structures supported on it. The increase in water content produces a permanent change in loess soil microstructure and sudden volume reductions. In this work, a hydro-mechanical (H-M) model is presented to analyze the soil response and the behavior of strip foundations on loess soil using a 2D finite element model. Numerical models were calibrated, on one hand, from experimental data of double oedometer tests and soil water retention curve; and on the second hand, by retrofit analysis from the measurement of settlements in fullscale foundation prototypes over collapsible loess during controlled water pipe leakage. The settlements were studied by performing statistics, sensibility, and inverse analyses. Final settlements after 2 years of infiltration of water were computed using Monte Carlo simulations. The results show that the compression index at saturation is the most significant parameter controlling the behavior of collapsible loess. Optimum sets of parameters were achieved with inverse analysis, demonstrating the accuracy of the H-M model to predict collapse-induced settlements of shallow foundations.