Search Results (22)

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

Predicting rock failure under excavation-induced non-uniform stress remains challenging due to the inability of conventional homogeneous specimens to replicate field-scale stress gradients. A novel trapezoidal sandstone specimen with adjustable top-surface inclinations (S75/S85) is proposed, uniquely simulating asymmetric stress gradients to mimic excavation unloading. Geometric asymmetry combined with multi-scale characterization (CT, SEM, PFC) decouples stress gradient effects from material heterogeneity. The key findings include the following points. (1) Inclination angles > 15° reduce peak strength by 24.2%, transitioning failure from brittle (transgranular cracks > 60) to mixed brittle-ductile modes (2) Stress gradients govern fracture pathways: transgranular cracks dominate high-stress zones, while intergranular cracks propagate along weak cementation interfaces. (3) PFC simulations reveal a 147% stress disparity between specimen sides and validate shear localization angles θ = 52° ± 3°), aligning with field data. This experimental–numerical framework resolves limitations of traditional methods, providing mechanistic insights into non-uniform load-driven failure. The methodology enables targeted support strategies for deep asymmetric roadways, including shear band mitigation and plastic zone reinforcement. By bridging lab-scale tests and engineering stress states, the study advances safety and sustainability in deep roadway excavation. Full article

Green concrete uses incinerator ash or lightweight ash as a substitute for cement. It retains the properties of conventional concrete. Initial laboratory tests have determined the optimum mix design, weight variation, and compressive strength. Defined as an environmentally friendly material, green concrete reduces pollution or improves environmental conditions during production. This study incorporates incinerator ash, a toxic byproduct of waste disposal, into concrete production through a phased laboratory and numerical approach. A database for deep learning modeling was created using Convolutional Neural Networks (CNNs) and the Multi-Verse Optimizer (MVO) algorithm. After evaluating the efficiency and structure of the deep learning model through MATLAB coding, the focus shifted to analyzing the sensitivity of the input parameters on the output parameter using MATLAB for coding, training, and evaluation. The initial results indicate a significant effect of incinerator ash on the compressive strength of concrete. In addition, the deep learning modeling results show that the regression coefficient (R) of 90% reflects the accuracy and efficiency of the deep learning model for the current mix design. The error index, which is also reported, shows that the applied deep learning modeling method achieves optimal performance, with an average error of 0.14. The sensitivity analysis results of the introduced optimal model show that among the five input parameters, cement weight (W) has the greatest influence on compressive strength, as indicated by the statistical group distances from the baseline, percentage values, and average values. Full article

The Luhai uranium deposit is a large-scale uranium deposit newly discovered in recent years through comprehensive prospecting methods. It is located in the Basaiqi Paleochannel Uranium metallogenic belt of the Erlian Basin and is characterized by its shallow burial and large scale. This paper provides new data on the genetic processes of sandstone-type uranium mineralization through sedimentological and geochemical environmental indicators (such as Fe³⁺/Fe²⁺, organic carbon, total sulfur, etc.), analysis of C-O isotopes of carbonate cements and H-O isotopes of groundwater, and geochemical and mineralogical studies of uranium minerals, iron–titanium oxides (involving backscatter analysis, micro-area chemical composition determination, and elemental surface scanning), and organic matter. Sedimentological analysis shows that the ore- bearing layer in the upper member of the Saihan Formation developed a braided channel within floodplain subfacies, which control the distribution of uranium ore bodies. Uranium mineralogical observations, geochemical environmental indicators, and organic geochemical data indicate that the main reducing agents related to mineralization are pyrite, terrestrial plants, and deep-sourced oil and gas. The δD values of groundwater in the ore-bearing layer range from −95.34‰ to −90.68‰, and the δ¹⁸O values range from −12.24‰ to −11.87‰. For calcite cements, the δ¹⁸O_V-PDB values range from −24‰ to −11.5‰, and the δ¹⁸_OV-SMOW values range from 6.2‰ to 19‰. It was determined that the ore-forming fluid is mainly surface fresh water that entered the strata during the tectonic uplift stage, with local mixing of deep-sourced brine. Based on these data, the main modes of uranium mineralization in the paleochannel were obtained as follows: (1) Redox mineralization occurs due to the reducing medium within the sand body itself and the reduction caused by deep- sourced oil and gas generated from the Tengge’er and Arshan Formations. (2) Mineralization is achieved through the mixing of fluids from different sources. Furthermore, a genetic model related to uranium mineralization in the paleochannels of the Luhai area has been established: favorable uranium reservoirs were formed during the sedimentary period, and during the post-sedimentary stage, reverse structures promoted redox reactions and fluid-mixing-induced mineralization. The research findings can provide guidance for the exploration of paleochannel sandstone-type uranium deposits in other areas of the Erlian Basin. Full article

Accurate estimation of concrete compressive strength is very important for the improvement of mix design, quality assurance, and compliance with engineering specifications. Most empirical traditional models have failed to capture the complex relationships inherent within varied constituents of concrete mixes. This paper develops a machine learning model for compressive strength prediction using mix design variables and curing age from a “Concrete Compressive Strength Dataset” obtained from the UCI Machine Learning Repository. After comprehensive data preprocessing and feature engineering, various regression and classification models were trained and evaluated, including gradient boosting, random forest, AdaBoost, k-nearest neighbors, linear regression, and neural networks. The gradient boosting regressor (GBR) achieved the highest predictive accuracy with an R² value of 0.94. Feature importance analysis showed that the water–cement ratio and age are the most crucial factors affecting compressive strength. Advanced methods such as SHapley Additive exPlanations (SHAP) values and partial dependence plots were used to attain deep insights about feature interaction with a view to enhancing interpretability and fostering trust in models. Results highlight the potential of machine learning models to improve concrete mix design with the aim of sustainable construction through the optimization of material usage and waste reduction. It is recommended that future research be undertaken with expanding datasets, more features, and richer feature engineering to enhance predictive power. Full article

The Deep Cement Mixing Integrated Drilling, Mixing and Jetting (DMJ) method represents a novel approach to the construction of slurry piles in the Yellow River Floodplain, offering the potential to enhance the quality of these structures. In order to investigate the pile strength and its distribution characteristics under different conditions, an unconfined compressive strength test was conducted on DMJ pile core samples. The Kolmogorov-Smirnov (K-S) test was employed to evaluate the normal distribution characteristics of the strength, and the fluctuation of the pile strength was evaluated by the autocorrelation function method to elucidate the distribution characteristics. Moreover, a resistivity non-destructive test was conducted to ascertain the correlation between strength and resistivity and to perform supplementary strength testing. The distribution of the pile body strength is normal at the 5% level of significance. A clear correlation was observed between the strength of the pile core and depth, while the correlation between the strength of the pile outer side and depth was less pronounced. Additionally, a positive linear correlation was observed between resistivity and strength, which can be used to evaluate the strength of DMJ piles. Full article

This research focuses on a mineral–cement mixture containing bitumen emulsion, designed for cold recycling procedures, the formulation of which includes 80% (m/m) of waste material. Deep cold recycling technology from the MCE mixture guarantees the implementation of a sustainable development policy in the field of road construction. The utilised waste materials include 50% (m/m) reclaimed asphalt pavement (RAP) from damaged asphalt layers and 30% (m/m) recycled aggregate (RA) sourced from the substructure. In order to assess the possibility of using a significant amount of waste materials in the composition of the mineral–cement–emulsion (MCE) mixture, it is necessary to optimise the MCE mix. Optimisation was carried out with respect to the quantity and type of binding agents, such as Portland cement (CEM), bitumen emulsion (EMU), and redispersible polymer powder (RPP). The examination of the impact of the binding agents on the physico-mechanical characteristics of the MCE blend was performed using a Box–Behnken trivalent fractional design. This method has not been used before to optimise MCE mixture composition. This is a novelty in predicting MCE mixture properties. Examinations of the physical properties, mechanical properties, resistance to the effects of climatic factors, and stiffness modulus were conducted on Marshall samples prepared in laboratory settings. Mathematical models determining the variability of the attributes under analysis in correlation with the quantity of the binding agents were determined for the properties under investigation. The MCE mixture composition was optimised through the acquired mathematical models describing the physico-mechanical characteristics, resistance to climatic factors, and rigidity modulus. The optimisation was carried out through the generalised utility function U^III. The optimisation resulted in indicating the proportional percentages of the binders, enabling the assurance of the required properties of the cold recycled mix while utilising the maximum quantity of waste materials. Full article

Foundation treatment piles are crucial for enhancing the bearing capacity and stability of weak foundations and are widely utilized in construction projects. However, owing to the complexity of geological conditions, traditional construction methods fail to meet the demand for low-carbon development. To address these challenges, this study introduced a comprehensive decision-making approach that considers the impact of stratum variability on greenhouse gas (GHG) emissions and pile bearing capacity from the design phase. During the design process, the GHG emissions and bearing capacities of deep cement mixing (DCM) and high-pressure jet grouting (HPJG) piles were quantitatively assessed by analyzing the environmental and performance impacts of foundation treatment piles related to materials, transportation, and equipment usage. The results suggest that the bearing capacity of piles in shallow strata is highly susceptible to stratum variability. Using piles with a diameter of 800 mm and a length of 20 m as an example, compared with DCM piles, HPJG piles demonstrated a superior bearing capacity; however, their total GHG emissions were 6.58% higher, primarily because of the extensive use of machinery during HPJG pile construction. The GHG emissions of foundation treatment piles in shallow strata were influenced more by geological variability than those in deep strata. Sensitivity analysis revealed that the pile diameter is a critical determinant of GHG emissions and bearing capacity. Based on the bearing capacity–GHG emission optimization framework, a foundation treatment strategy that integrates overlapping and spaced pile arrangements was introduced. This innovative construction method reduced the total GHG emissions by 22.7% compared with conventional methods. These research findings contribute to low-carbon design in the construction industry. Full article

The objective of this article is to assess the carbon footprint across the Continuous Deep Mixing Method (CDMM) life cycle, considering its implementation in the context of sustainable, zero-emission, and decarbonising construction. Amidst global climate change challenges of greenhouse gas emissions in the construction sector, the CDMM emerges as a potentially effective solution to mitigate environmental impact. This study aims to address the gap in the existing scientific literature by evaluating the environmental aspects of CDMM application, with a focus on identifying primary emission sources. This research extends beyond the conventional focus on construction materials to include energy consumption from equipment and transportation, offering a holistic view of the technology’s environmental impact. This analysis identified cement as the major greenhouse gas emission source for the CDMM, underscoring the technology’s potential as an alternative to traditional geotechnical methods, in line with integrated design solutions and meeting growing social expectations for sustainability. The added value of this study comes from data derived from an actual project, enabling a realistic assessment of CDMM’s environmental impact and resource and energy efficiency. Full article

Recent decades have witnessed the increasing usage of deep cement mixing (DCM) mixers in the field of marine infrastructure construction. The mixing performance, including the torque history, can be helpful for structural safety evaluation, design, and the optimization of agitators, which is of engineering significance. However, to the best of the authors’ knowledge, there are no related publications that have reported the mixing behaviors of deep cement mixing agitators. In light of this, the present work conducts experimental and numerical investigations of the mixing behaviors of a DCM ship mixing agitator. To achieve this end, a model test device is established, and mixing experiments using two- and three-blade mixers are respectively conducted. Silt and clay soils are considered in the experiments with a three-blade mixer, while clay soils are used for those with a two-blade mixer. In addition, this work designs a torque transducer placed inside the rotating rod to accurately measure the torque history of the agitator during model test experiments. The experimental results show that, when mixing clay using agitators with different blades, the average torque value required for a two-blade agitator is slightly larger than that for a three-blade one. This study also presents a computational framework based on the arbitrary Lagrangian–Eulerian (ALE) method for an efficient and accurate modeling of the soil-mixing behaviors of the agitator. The numerical results are found to be in good agreement with the experimental data from model tests in terms of torque history, which demonstrates the effectiveness and capacity of our presented computational framework. The numerical results show that the average torque value is smaller at a higher rotational speed during the mixing of clay using a two-bladed agitator, but the effect of rotational speed on the torque history is small. The experimental and numerical methods introduced in the present work can act as a useful tool for investigations of mixing behaviors of DCM agitators. Full article

Stiffened deep cement mixing (SDCM) piles are composite piles that combine the advantages of single large-diameter deep cement mixing (DCM) and precast concrete piles. They comprise precast concrete piles as the core and cast-in-place DCM piles as the outer layer. This study evaluates the bearing characteristics of SDCM piles under vertical loading. The composite modulus of elasticity of SDCM piles is first introduced and determined using the area-weighted average method. Then, the reliability of the proposed method is described by comparing the calculated results with the findings of the existing literature. Furthermore, a nonlinear simplified analysis method based on the load transfer method is proposed for vertical bearing characteristics of equal- and short-core SDCM piles under vertical loading. This method is developed by the finite difference method. The accuracy of the simplified method is validated by comparing its results with those from existing tests, theoretical analysis, and finite element simulations. The results of their study indicated that the area-weighted average method calculates the composite modulus of elasticity of the composite pile section of the SDCM piles with an error below 0.5% compared to the analytical method. This finding represents sufficient accuracy. The simplified calculation method established in this study is computationally stable. When the iteration factor is set to 10⁻⁶, as the number of discrete nodes n on the pile increases, the calculation results are stable with a good convergence when n > 30. The vertical bearing capacity and pile top stiffness of SDCM piles increased with the length of the core piles. There was a reasonable core-to-length ratio for SDCM piles in specific scenarios. An excessively long DCM pile section made its lower part force-free for a given length of core piles. The appropriate length of core piles should be determined in actual projects to avoid unnecessary material waste. An optimum ratio of core piles for SDCM piles was determined. Beyond this optimal value, an increase in the ratio of core piles controlled the pile settlement in a limited manner. Full article

Constructing embankments over soft soils is a challenge for geotechnical engineers due to large settlements. Among diverse ground-improvement methods, combining piles and geosynthetics (e.g., geosynthetic-reinforced piles, deep cement mixing columns, geotextile-encased columns) emerges as a reliable solution for time-bound projects and challenging ground conditions. While stress distribution within pile-supported embankments has been extensively studied, the load transfer efficiency of piled solutions with geosynthetic reinforcement remains less explored. The novelty in this study lies in the investigation of three different inclusion solutions from a common control case in the numerical model considering the role of geosynthetic reinforcement. This study investigates the load transfer mechanisms in embankments supported by various techniques including geosynthetic-reinforced piles, deep cement mixing columns, and geosynthetic-encased granular columns. Two-dimensional axisymmetric finite element models were developed for three cases of embankments supported by vertical inclusions. Numerical findings allow clarification of the soft ground and embankment characteristics which influence the arching and membrane efficiencies. Rigid piles outperform deep cement mixing (DCM) columns and geotextile-encased columns (GEC) in reducing settlements of soft ground. Geosynthetic reinforcements are particularly helpful for rigid pile solutions in high embankments due to their load transfer capability. Additionally, physical properties of fill soil can impact the inclusion solutions, with high shear resistance enhancing the arching effect and lower modulus subsoils showing better arching performance. Full article

Saline soils belong to the category of problematic soils with high compressibility and weak shear strength when exposed to water. Water dissolves the salts in soils which are the primary cementing agents. Therefore, stabilization methods that provide sustainable cementing substances are employed in this study using deep soil mixing techniques to enhance the properties of saline soil. In this regard, a laboratory-scaled deep soil mixing procedure was developed to treat the soil in a way similar to the field methods. A binder, consisting of marble powder and cement, was employed to treat the soil. This study aimed to select the most efficient binder mix design in terms of optimum marble powder/cement ratio and optimum water/binder ratio. Unconfined compressive strength, durability, density measurements and ultrasonic velocity pulse tests were conducted on the treated soil. To determine the treatment efficacy, microstructure analysis of the treated samples was conducted. The 80C20MP and 70C30MP samples exhibit a dense soil structure with minimal voids, and their microstructure is denser than the other treated specimens. Additionally, the EDX analysis shows increased calcium percentages with up to 30% MP replacement, aligning well with the microstructure analysis and the UCS values. The results indicate that the economical and eco-friendly binder mix consisted of (70% to 80%) cement and (20% to 30%) marble powder with water/binder ratio in the range of 1.1 to 1.3. This mix contributed greatly to the improvement in soil strength and integrated columns. Full article

As an effective technology to reduce carbon dioxide emissions, carbon capture, utilization, and storage (CCUS) technology has been a major strategic choice and has received widespread attention. Meanwhile, the high cost and strict requirements of carbon dioxide storage and utilization on geographical conditions, industrial equipment, and other aspects limit large-scale applications of CCUS. Taking Shanxi Province as an example, in this paper, we study the economic and environmental characteristics of carbon dioxide capture, storage, and utilization under different combinations of technical routes. Steel, power, cement, and chemical industries are considered. Deep saline aquifers and CO₂-enhanced coalbed methane (CO₂-ECBM) recovery are selected as the two types of sequestration sinks. Urea production, methanol production, microalgae cultivation, and cement curing are selected as the four potential utilization methods. Then, a mixed-integer linear programming (MILP) model is used to optimize the CO₂ utilization pathway based on the principle of least cost, to select the best emission sources, CO₂ pipelines, intermediate transportation nodes, utilization, and storage nodes to achieve reasonable deployment of CCS/CCU projects in Shanxi Province. The results show that CCU with urea production has the lowest cost and is the most economically viable with over 50% reduction in emissions. The second option is CCS which includes CO₂-ECBM and achieves a 50% reduction in emissions. In addition, there is little difference between the cost of cement-cured CCU and that of methanol-produced CCU. CCU for microalgae cultivation has the highest cost. Therefore, the latter three utilization pathways are currently not economical. Full article

Microbially induced carbonate precipitation (MICP) is a new method used in recent years to improve the soil. However, this method still faces challenges related to low grouting reinforcement strength and efficiency. In this study, both the bio-cement infiltration method and bio-cement mixed method for sand foundation were proposed, and physical model tests were conducted to investigate the mechanical properties of sand treated with the bio-cement method. The results showed that the bio-cement maximized the utilization rate of bacterial liquid and reduced the waste caused by the loss of bacteria compared with traditional methods. Both the size of the reinforced area and bearing capacity of the sand reinforced by bio-cement infiltration method were controlled by the volume ratio of the bio-cement, calcareous sand powder, and the inflow rate. The maximum bearing capacity was 125 N when using a mixture of bio-cement and calcareous sand powder with a ratio of 400/80, with an inflow rate of 20 mL/min. The UCS of the sand reinforced by the bio-cement mixed method gradually decreased from 3.44 MPa to 0.88 MPa with depth, but increased with increasing CaCO₃ content. The CaCO₃ crystals were primarily concentrated at the contact point between the particles, and the formed crystals were mainly polyhedral. Reduction in the CaCO₃ content mainly occurred in the central deep part of the reinforcement area. The result provides an experimental basis for the use of bio-cement in the reinforcement of sand soil foundations. Full article