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Soft soils pose significant challenges to the environment and construction of infrastructure on them owing to their distinct characteristics such as low bearing strength, high water content, low permeability, and high void ratio. The stiffness modulus of soft ground soils (Gs) is one of the major considerations while designing geo-structures. The determination of the stiffness modulus of soft ground materials such as soils requires expensive machinery, more skilled labor, and consumption of time which is contrary to the current trends of sustainable development. Therefore, this paper presents the artificial intelligence (AI)-based sustainable solutions for the estimation of Gs using artificial neural network (ANN), gene expression programming (GEP), and multiple linear regression (MLR) techniques. In this regard, 199 samples of soft soil from different locations were retrieved and tested to determine basic soil attributes such as sand content (S), fine content (FC), liquid limit (LL), plastic limit (PL), water content (w), and bulk density (d) which were used as potential indicators for computing soft ground stiffness modulus. Many statistical tests, including R-square (R2), root means square error (RMSE), and mean absolute error (MAE), were used to further substantiate the performance efficiency of computed prediction models. The findings show that the proposed models meet all accuracy-related acceptance requirements. However, ANN outperforms GEP and MLR. Further, to evaluate the specific impact of input factors, sensitivity and parametric tests were also executed.

•Investigate how consolidating state affects shield tunneling-induced horizontal displacement.•Simulate the magnitude and distribution of long-term horizontal displacement numerically.•Compare the differences in long-term horizontal displacement between normally-consolidated and consolidating cases.•Propose an empirical method to estimate long-term horizontal displacement in consolidating soft ground. Shield tunneling often gives rise to excessive long-term horizontal displacement in consolidating soft ground, posing risks to the safety of adjacent structures. This study investigates the characteristics of long-term horizontal displacement induced by shield tunneling in consolidating soft ground, with the aim of providing practical guidance for optimizing ground treatment strategies. Firstly, a three-dimensional numerical model, validated by a case history in Shanghai, is employed to analyze the horizontal displacement of the soft ground. Comparisons are conducted between the horizontal displacements in normally-consolidated and consolidating cases. Subsequently, the influence of the consolidating state on the horizontal displacement is investigated by numerical analyses. The simulation results indicate that the short-term horizontal displacements follow a similar trend and comparable magnitude in both normally-consolidated and consolidating soft soil. However, the long-term horizontal displacements display a quite different pattern. The maximum discrepancy between normally-consolidated and consolidating cases is observed at the ground surface, where the long-term horizontal displacements of the two cases orient toward entirely opposite directions. The discrepancy at the ground surface increases as the degree of consolidation or the tunnel depth decreases, while it is relatively insensitive to the thickness of the newly filled layer. Finally, an empirical estimation method is proposed to predict the long-term horizontal displacement at the ground surface for shield tunneling in consolidating soft ground.

In tunnel construction, efficiently predicting the energy usage of tunnel boring machines (TBMs) is critical for optimizing operations and reducing costs. This research proposes a novel method for predicting the specific energy of micro slurry tunnel boring machines (MSTBMs) using an explainable neural network (xNN) that leverages operator-monitored data. The xNN model provides transparency and interpretability by integrating the Shapley additive explanation (SHAP) technique, enabling tunneling engineers and operators to gain valuable insights into the prediction process. Extensive data from MSTBM umbrella pipe support excavation are the foundation for training, testing, and unseen data in the xNN model. The specific energy formula derived from the operational parameters of the MSTBM defines the dependent variable for the xNN model. The test dataset evaluates the model’s performance with an R ² of 98.7%, an MSE of 2.40, and an MAE of 0.003, demonstrating its accuracy and reliability. Ten percent of the dataset was reserved as unseen data to assess the model’s generalization capabilities. Upon evaluation, the model achieved an R 2 value of 89%, an MAE of 0.01, and a root mean squared error (RMSE) of 0.01. The xNN empowers operators to optimize operational parameters and promote more efficient and sustainable tunneling practices by identifying influential factors affecting energy consumption through its interpretable nature. This research has significant implications for the future of underground construction, paving the way for improved resource management.

This study investigated the compressibility of clay ( C c ) for soft ground improvement and developed six optimized metaheuristic-based extreme learning machine (ELM) models (particle swarm optimization (PSO)-ELM, moth search optimization (MSO)-ELM, firefly optimization (FO)-ELM, cuckoo search optimization (CSO)-ELM, bees optimization (BO)-ELM, and ant colony optimization (ACO)-ELM) to predict C c . A total of 739 laboratory tests were conducted to develop the models, and 517 datasets were used for training, while the remaining 222 samples were used for testing. The results showed that the accuracy of the developed models was improved by 3–5% compared to the original ELM model. The BO-ELM and MSO-ELM models were identified as the most effective models for predicting C c , with accuracies ranging from 86.5% to 87%. The study suggests that the MSO-ELM model should be used if training time is critical. The developed models provide useful tools for predicting C c , an essential parameter for soft ground improvement design, and can assist in the improvement of soft ground.

Jang, J.; Jang, J.; Barbato, M.; Gutierrez-Wing, M.T.; Rusch, K.A., and Jung, J., 2021. Effects of fluorogypsum and quicklime on unconfined compressive strength of Kaolinite. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 126–130. Coconut Creek (Florida), ISSN 0749-0208. Coastal areas have environmentally and economically important roles but tend have weak soft ground, which is often vulnerable by waves and unsuitable for coastal construction, such as ports and waterfront areas. Hence, this soft ground, which usually contains large amounts of clays, needs to be ameliorated by using appropriate soil improvement techniques. A common approach to improve soft ground is soil–binder injection techniques to enhance its strength. When avaialbe, binders from industrial wastes can be used instead of commercial products, such as cement and lime, to reduce construction costs and minimize environmental disturbance. Reusing industrial wastes mitigates environmental pollution and reduces the costs of waste management. Construction materials, such as sand and cement, can be partially replaced with industrial wastes if the wastes are granular and induce cementation effects. Fluorogypsum (FG), a by-product obtained during the production of hydrofluoric acid, satisfies these conditions, as it is capable of binding granular materials. Approximately 894,000 metric tons are annually produced in the U.S. However, data on the mechanical strength of clay–FG mixtures are unavailable. In this study, we conducted unconfined compressive strength tests to investigate the mechanical behavior of kaolinite, which represented clay in soft ground, at different FG and quicklime contents. The effects of FG on the compressive strength of kaolinite–FG–quicklime mixtures depend on the curing time and weight ratios of the constituent materials. The composition of the mixture with the highest compressive strength was 30% FG, 5% lime, and 65% kaolinite. We infer that the stoichiometric ratios of mixtures control the chemical reactions for the maximum compressive strength at different quicklime contents based on a series of compressive tests.

By comparing different settlement forecast methods, eight methods were selected considering the creep of marine soft soils in this case study, including the Hyperbolic Method (HM), Exponential Curve Method (ECM), Pearl Growth Curve Modeling (PGCM), Gompertz Growth Curve Modeling (GGCM), Grey (1, 1) Model (GM), Grey Verhulst Model (GVM), Back Propagation of Artificial Neural Network (BPANN) with Levenberg–Marquardt Algorithm (BPLM), and BPANN with Gradient Descent of Momentum and Adaptive Learning Rate (BPGD). Taking Lingni Seawall soil ground improved with prefabricated vertical drain-assisted staged riprap filling as an example, forecasts of the short-term, medium-term, long-term, and final settlements at different locations of the soft ground were performed with the eight selected methods. The forecasting values were compared with each other and with the monitored data. When relative errors were between 0 and −1%, both the forecasting accuracy and engineering safety were appropriate and reliable. It was concluded that the appropriate forecast methods were different not only due to the time periods during the settlement process, but also the locations of soft ground. Among these methods, only BPGD was appropriate for all the time periods and locations, such as at the edge of the berm, and at the center of the berm and embankment.

The test methods provided by current related \"specifications\" do not apply to saline soil soft foundation bearing capacity test. Through discussing the limitations of the related specifications and based on the experience on saline soil soft ground capacity test, the paper made some improvements of test on such aspects: conditions of loading and stopping load, determination of the characteristic value of the ground bearing capacity and evaluation, the paper also put forward the saline soil soft ground capacity test method.

There are many engineering characteristics for soft ground. It is an urgent problem for engineering construction to predict settlement according to the features of soft ground and existing settlement data. In this paper, we used the measured settlement data of soft ground, then come up with the prediction method of soft ground settlement based on the BP artificial neural network in the Matlab simulation environment. This method can modelling highly complex and nonlinear earth structures directly based on real samples. Practical test showed that this method has a high degree of accuracy, and it is very valuable to instruct construction and guarantee the stability of the ground.

TBM vibration is inevitable during excavation in hard rock or mixed face ground conditions (MFC) and is detrimental to equipment safety and environmental protection. However, the cutting-induced vibration can help clarify the TBM-ground interaction and provide a valuable ground identification approach. In the present investigation, a field measurement of TBM vibration in changing ground conditions was carried out with accelerometers mounted on the TBM bulkhead. The vibration characteristics and patterns under different ground conditions were compared by signal processing, and their relationships to the operating parameters were investigated. The results showed that the TBM dynamic response was highly dependent on geological conditions. In homogeneous soft ground (HSG), the magnitude of vibration was low and stable, while under the MFC, the high frequency and strong vibration occurred. The vibration waveform in the MFC had an apparent periodicity and was consistent with the cutter head rotation speed. In addition, the signal consisted of a series of periodical impulses with intervals, revealing the rock-cutting process. Since the TBM vibration was sensitive to ground changes, it could provide valuable and precise information about the cutting face ground conditions. This investigation of the relationship between the ground and vibration can provide a foundation for vibration-based ground identification, which is a potential application in simultaneously tracking geological conditions during tunneling.

This paper presents the field application of an innovative method called full displacement pipe jacking (FDPJ) for installing small-diameter underground pipelines in challenging ground conditions, such as loose sand and soft clay with high groundwater levels in Indonesia. Unlike conventional techniques that require soil excavation using cutterhead machines and the transport of large volumes of excavated soil (spoil), the FDPJ uses a conical expander to laterally displace the soil and create space for the pipeline. This eliminates spoil handling while improving ground stability and reducing environmental impact. This paper describes the FDPJ installation process in detail and presents field performance data on construction time and surface impact. On average, each 50 m – 70 m pipeline span was completed in approximately five days, with ground deformation limited to ±3 mm. These results indicate that FDPJ is a practical, eco-friendly, and efficient alternative for underground pipeline installation, particularly in urban areas where minimizing surface disturbance is critical.