Structural damage and foundation leakage are major concerns for earthen dams. To minimize seepage, cutoff walls are typically installed beneath the dam core to act as impermeable barriers. While concrete cutoff walls are widely used, their limited ductility and strength incompatibility with foundation soil present design challenges. Plastic concrete, a modified form of conventional concrete incorporating bentonite and pond ash, offers improved ductility and reduced brittleness, making it a suitable alternative. This study investigates the use of pond ash-based flowable fill as a replacement for normal concrete in plastic concrete cutoff walls. The unconfined compressive strength (UCS) of plastic concrete mixes was analyzed using four advanced regression machine learning algorithms: multivariate adaptive regression splines, extreme neural network (ENN), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). Several performance indices were used to evaluate model accuracy. The MARS model achieved the highest accuracy, with R2 = 0.990 for training and R2 = 0.963 for testing, followed by XGBoost, GBM, and ENN. SHAP analysis revealed that curing period has the most significant positive effect on UCS, followed by water and cement contents, while bentonite showed the least impact. Key properties were evaluated to determine an optimal mix design. This research enhances the understanding of CLSM-based plastic concrete and supports its application in cutoff walls by developing accurate UCS prediction models, contributing to the improved suitability and sustainability of dam foundation systems.

Vegetated blue carbon environments have the potential to sequester large amounts of carbon due to their high productivity and typically saturated, anaerobic soils that promote carbon accumulation. Despite this, and the coupling of Fe-S-C cycling processes, the influence of iron (Fe) in acid sulfate soils (ASSs) on carbon sequestration in blue carbon environments has yet to be systematically explored. To address this knowledge gap, this review provides an overview linking the current state of blue carbon studies with the influence of Fe on soil organic carbon (SOC), as well as the potential influence ASSs have on carbon sequestration. A systematic literature review on SOC stock in blue carbon studies using the Web of Science database yielded 1477 results. Studies that investigated the drivers of carbon accumulation in blue carbon studies were restricted to vegetation species/structure and geomorphic setting, and few focused on soil properties and type. Iron both protects and enhances SOC decomposition depending on its redox state. Under oxic conditions, Fe oxyhydroxides can protect SOC via adsorption, co-precipitation and by acting as a cement in soil aggregates. Iron can also increase SOC decomposition under oxic conditions due to Fenton reactions. However, under anoxic conditions, SOC mineralisation can also occur as Fe acts as an electron transporter in dissimilatory reductions. ASSs contain a range of Fe minerals, but the oxidation of Fe sulfides can result in soil acidification (pH < 4) and subsequent impacts, such as a decline in vegetation health, poor water quality and infrastructure damage. Therefore, potential SOC protection by Fe under oxic conditions may come at the cost of soil acidification in ASSs, while maintaining anoxic conditions prevents acidification but may enhance SOC decomposition. Future studies on the influence of ASSs on Fe-S-C cycling and carbon sequestration in blue carbon environments are important, particularly for 'hotspots' such as Australia.

Venice, the enchanting Italian city built on a lagoonal environment, faces ongoing geotechnical challenges due to natural processes and anthropogenic influences. Over the past century, extensive geotechnical investigations have been conducted to characterize the unique stratigraphy of Venice's soils. Some key locations, representative of the city's diverse soil profiles, have undergone in-depth analysis, with investigations reaching depths of tens of meters. Three key sites-Malamocco, Treporti, and La Grisa-were strategically selected to study the complex mechanical properties of Venetian soils. In this study, we present a comprehensive synthesis of the most significant findings from the geotechnical investigations conducted throughout the Venetian lagoon over recent decades, focusing on methodologies for the evaluation of stiffness parameters in highly heterogeneous soil layers. These results enhance the understanding of geological and geotechnical behaviour of Venice's subsoil and provide crucial data for developing resilient engineering solutions.

This article presents a series of cyclic triaxial tests to investigate the particle breakage characteristics of coarse-grained filler under heavy-haul train load. The results show that the main patterns of particle breakage for large-sized particles (the particle size between 22.4 and 31.5 mm) are fracture and abrasion, and the particle breakage makes the outer contour of the particle closer to the sphere. The particle breakage is found in the process of vibratory compaction of specimens, and the particle breakage caused by isotropic consolidation under low confining pressure (no more than 300 kPa) can be ignored. It is also found that significant particle breakage occurs during cyclic loading, characterized by the reduction of the large-sized particles (the particle size between 16 and 31.5 mm) and the increase of fines content. In addition, further particle breakage is caused by the increase in the cyclic stress ratio. Based on the test results, a power function equation of Marsal's breakage factors and cyclic stress ratio is proposed.

Geopolymers made from simulated Martian regolith would suffer from exhibit poor engineering properties, rendering them unsuitable as base materials. To improve the mechanical properties of geopolymers, this study prepared a new Martian soil simulant named DH-1. DH-1- based geopolymers were synthesized using Al2O3 (Al group) and metakaolin (MK group) as modifying agents. The durability of geopolymers with ultraviolet (UV) radiation was assessed under simulated Martian atmospheric conditions. The results indicated that the UCS and FS of both the Al and MK groups increased with curing time, with maximum UCS and FS of 55.27 MPa and 15.16 MPa, respectively. The UCS and FS of the Al and MK groups exhibited a two-phase change. The inflection points for rapid to slow in the UCS and FS occurred on day 28 for the Al group and day 14 for the MK group. The addition of Al2O3 and metakaolin promoted the replacement of silicon atoms by aluminum atoms in the silicaoxygen group, producing more gels product. After UV irradiation, the UCS and FS of the geopolymer decreased by 13 % and 44 %, respectively. Furthermore, the geopolymers underwent carbonation, with new cracks forming due to the combined effects of UV exposure, causing strength reduction.

Human space exploration missions in the near future will inevitably demand beyond-Earth manufacturing capacity to develop critical subsystems utilising in situ resources. Therefore, to find an alternative solution to the logistics challenges of long-duration space missions, an on-site component fabrication process utilising indigenous resources on the Moon and Mars will be economical and play a crucial role in ensuring the expansion of succeeding missions to deep space. Additive manufacturing (AM) exhibits excellent potential to develop intricate components with functional and tailorable properties at various scales. To assess the potential of AM, an artificial Mars soil has been processed to formulate stable aqueous paste containing less organics (1.5% versus typically 30-40%) amenable to resource-efficient 3D printing. The formulated paste was utilised to fabricate a range of solid and porous designs of various shapes and sizes using a layer-wise material extrusion method for the first time. The additively manufactured components sintered at 1100 degrees C for 2 h exhibited an average relative permittivity (epsilon r) = 4.43, dielectric loss (tan delta) = 0.0014, quality factor (Q x f) = 7710 GHz and TCf = - 9. This work not only demonstrates progress in paste additive manufacturing but also illustrates the potential to formulate eco-friendly ink that can deliver components with functional properties to support long-term space exploration utilising local resources available on Mars.

积雪面积比例FSC(Fractional Snow Cover)能在亚像元尺度上定量描述积雪的覆盖程度，相比二值积雪更适合反映复杂山区积雪的分布情况，是山区融雪径流模拟，气候变化预测的重要输入参数。本研究在亚洲高山区HMA(High Mountain Asia)基于分地类特征选择的多元自适应回归样条MARS(Multivariate Adaptive Regression Splines)模型LC-MARS发展了MODIS FSC反演算法，并制备了亚洲高山区FSC产品。以Landsat 8提取的FSC为参考真值验证LC-MARS模型反演FSC精度，对比相同训练样本下LC-MARS模型与线性回归模型反演FSC精度，比较LC-MARS模型制备的FSC与MOD10A1、SnowCCI在亚洲高山区的精度表现。结果表明：（1） LC-MARS模型反演的FSC总Accuracy、Recall分别为93.4%、97.1%，总体RMSE为0.148,MAE为0.093，总体精度较高。（2）相同训练样本下LC-MARS模型在林区、植被和裸地反演FSC精度均高于线性回归模型，表明LC-MARS模型更适用于...

积雪面积比例FSC(Fractional Snow Cover)能在亚像元尺度上定量描述积雪的覆盖程度，相比二值积雪更适合反映复杂山区积雪的分布情况，是山区融雪径流模拟，气候变化预测的重要输入参数。本研究在亚洲高山区HMA(High Mountain Asia)基于分地类特征选择的多元自适应回归样条MARS(Multivariate Adaptive Regression Splines)模型LC-MARS发展了MODIS FSC反演算法，并制备了亚洲高山区FSC产品。以Landsat 8提取的FSC为参考真值验证LC-MARS模型反演FSC精度，对比相同训练样本下LC-MARS模型与线性回归模型反演FSC精度，比较LC-MARS模型制备的FSC与MOD10A1、SnowCCI在亚洲高山区的精度表现。结果表明：（1） LC-MARS模型反演的FSC总Accuracy、Recall分别为93.4%、97.1%，总体RMSE为0.148,MAE为0.093，总体精度较高。（2）相同训练样本下LC-MARS模型在林区、植被和裸地反演FSC精度均高于线性回归模型，表明LC-MARS模型更适用于...

The focus of the study is to examine the undrained behavior of twin circular tunnels in anisotropic and nonhomogeneous clays. To consider the effect of anisotropic soil, the popular anisotropic undrained shear (AUS) failure criteria are adopted in the study while the nonhomogeneous behavior is represented by linearly increasing strength with depth. Using Broms and Bennermarks' stability number, this study investigates the dependence of the undrained stability number N on four dimensionless input parameters, namely the isotropic ratio (re), the undrained shear strength gradient (rho D/suTC0), the cover depth ratio (C/D), and the spacing ratio (S/D). The effects of these four design parameters on the failure mechanism are also examined graphically. After being verified with previously published works, the comprehensive 1080 numerical results are then utilized as the dataset to create several machine learning models, including artificial neural network (ANN), support vector machine (SVM), and multivariate adaptive regression splines (MARS). The evaluating process by optimizing hyper-parameters reveals that the MARS model is a top competitor, providing considerable regression accuracy with a simple predictive function. The sensitivity analysis has also uncovered that both rho D/suTC0 and C/D have significant influences on the undrained stability number N, while comparing to re and S/D. The present study would provide many practical insights to the problem of twin circular tunnels in anisotropic and nonhomogeneous clays.

Deep excavations in urban areas may cause excessive ground deformations, leading to potential damage to the surrounding buildings. To prevent such problems, some techniques, such as the anchored wall systems, are used as a supporting method. This study, based on a large number of data produced by FE simulations and using the MARS approach, first presents a straightforward relation that can predict the maximum horizontal displacement of the anchored wall systems. Then, a simple predictive relation using MARS is established for developing a unique stiffness parameter. Finally, a novel design procedure based on the proposed MARS models, the stiffness parameter, geometric relationships, and recommendations from the FHWA is developed. By following this procedure, one can reasonably estimate the lengths of the anchors, their spacing, and tensile forces. Furthermore, the maximum horizontal displacement of the excavation wall can be accurately predicted and compared with the real value for better safety control of the construction procedure.