Particle Size Distribution Of Aggregates Research Articles

Preparation and Properties of Expansive Backfill Material Based on Municipal Solid Waste Incineration Fly Ash and Coal Gangue

To realize the large-scale utilization of municipal solid waste incineration (MSWI) fly ash in the field of building materials and to reduce the cost of coal mine backfill mining, the effects of the mixing ratio of cementitious materials, the particle size distribution of aggregates, and the amount and mass concentration of cementitious materials on the properties of backfill materials were experimentally investigated, and the microstructure of the hydration products was analyzed. The results showed that as the mass ratio of MSWI fly ash to bottom ash increased, the rate of expansion of the cementitious system continued to increase, and the compressive strength of the cementitious system continued to decrease. The Al (aluminum) and AlN (aluminum nitride) in the fly ash reacted with water to generate gas, causing the expansion of the cementitious materials; NaOH increased the alkalinity of the solution, which promoted the formation of more bubbles, thereby improving the expansion performance of the cementitious material. When the content of NaOH was 0.9%, the sample rate of expansion could reach 15.9%. The addition of CaCl2 promoted the early hydration reaction of the cementitious material, forming a dense microstructure, thus improving the early strength and rate of expansion of the cementitious material. The compressive strength of the backfill body increased as the fractal dimension of the aggregate particles increased, and the particle grading scheme of group S1 was optimal. The 1-day, 3-day, and 28-day strengths of the backfill body of group S1 reached 0.72 MPa, 1.43 MPa, and 3.26 MPa, respectively. It is recommended to choose a backfill paste concentration ranging between 78.5% and 80% and a reasonable amount of cementitious material between 20% and 25%. After the MSWI fly ash was prepared as a backfill material, the leaching of potentially harmful elements in the fly ash was greatly reduced, and the concentration of dioxin was reduced to 13 ng TEQ/kg. This was attributed to the dilution of the cement, the physical encapsulation of gel products, and the isomorphous replacement of Ca2+ in calcium aluminate chloride hydrate.

Long-term cotton stubble return and subsoiling improve soil organic carbon by changing the stability and organic carbon of soil aggregates in coastal saline fields

Cotton stubble return and subsoiling significantly enhanced soil fertility and cotton yield in coastal saline soils. Despite these benefits, the mechanisms influencing soil organic carbon (SOC) sequestration remain poorly understood. To address this gap, a comprehensive ten-year study was undertaken, focusing on the effects of stubble management (either removal or return) and tillage (either non-subsoiling or subsoiling) on the physicochemical properties of both topsoil (0–20 cm) and subsoil (20–40 cm) layers, as well as on cotton yield. The objectives were to (i) quantify interannual variability (2013–2022), stubble return, and subsoiling on the soil physicochemical properties of the topsoil (0–20 cm) and subsoil (20–40 cm) and cotton yield and (ii) elucidate the mechanism by which these agricultural practices affect SOC sequestration. Findings from this decade-long investigation indicated that ten consecutive years of cotton stubble return and subsoiling at three-year intervals significantly ameliorated soil conditions by reducing the salt content, pH, and the proportion of silt plus clay, while increasing macro-aggregates, mean weight diameter (MWD), aggregates organic carbon (AOC) across various particle sizes, SOC, total nitrogen (TN) in both soil layers, and ultimately, the cotton lint yield. Interestingly, the effect of stubble return on micro-aggregates varied between the two soil layers, leading to a reduction in micro-aggregates by 11.16% in the topsoil, in contrast to a 5.7% increase in the subsoil, when compared to conditions where stubble was removed. The partial least squares path modeling (PLS-PM) indicated that the enhancements in MWD attributable to stubble return and subsoiling were primarily due to significant reductions in soil pH and salt content in both layers, which in turn fostered an increase in AOC, ultimately contributing to higher SOC levels. However, the pathways through which AOC influenced SOC varied significantly between the topsoil and subsoil, with stubble return and subsoiling increasing SOC through an increase in the macro-AOC in the topsoil, and micro-AOC in the subsoil. In summary, long-term cotton stubble return and subsoiling significantly optimize the aggregate particle size distribution and enhance the carbon sequestration potential in coastal saline cotton fields.

A novel method for predicting coarse aggregate particle size distribution based on segment anything model and machine learning

Particle size distribution (PSD) is an important index property of granular materials. The conventional mechanical sieve analysis for PSD determination is labor-intensive, time-consuming, and inefficient. With the development of computer vision, image-based gradation prediction methods gradually emerge. However, previous studies mainly focused on images where individual aggregate particles are separated from each other without overlapping (i.e., not densely packed together), thus limiting their applicability. This study proposed a novel framework to predict the PSD of densely packed coarse aggregate particles, which is based on visual foundation model and machine learning with appropriate feature engineering. First, the segment anything model, which is one of the visual foundation models, was used for the instance segmentation of the densely packed aggregate particles. Then, the feature engineering was implemented with the segmentation result. The major quantitative indicators related to particle sizes were extracted from each mask, such as the area, the diameter of minimum enclosing circle, and the major axes of the equivalent ellipse of particle contours. Subsequently, the cumulative distribution curves of these indicators were plotted, followed by the data sampling operation on these curves to generate the feature vectors. The artificial neural network (ANN) model was then trained for gradation prediction with each of the feature vectors and the data points extracted from PSD curves used as the inputs and data labels, respectively. The model-training dataset consisted of 8160 different sets of digitally generated particle samples with known PSDs. Finally, verification test results of additional five different groups of digital particle samples and real coarse aggregate samples with known PSDs show that the root mean squared error (RMSE) of predicted aggregate sizes is 1.79 mm and 1.86 mm for digital and real aggregate particle samples, respectively. Therefore, the efficacy and feasibility of the proposed method was validated in terms of predicting the PSD of densely packed coarse aggregates from two-dimensional image information. As compared to the Mask RCNN combined with empirical formula of which the RMSE values were 5.12 mm and 5.84 mm for digital and real aggregate particle samples, respectively, the proposed framework demonstrates improved particle segmentation applicability and better PSD prediction accuracy.

Inferring primary particle size distribution and thermal accommodation coefficient of soot aggregate across diverse ambient temperatures using laser-induced incandescence

Laser-induced incandescence (LII) technique has proven to be a powerful tool for non-invasive, in-situ particle sizing of soot aggregates in flames, and the key lies in interpreting LII signals with a heat transfer model. However, the reliability of model-derived primary particle size distribution (PPSD) is sensitive to the uncertainty of thermal accommodation coefficient (TAC), which is typically regarded as a pre-determined parameter in the LII heat transfer model. To overcome this problem, we have proposed a trivariate inversion strategy, i.e., inferring two PPSD parameters and TAC simultaneously from LII signals, which has been theoretically validated at room temperature, but its feasibility in the flame case remains to be investigated. In this study, the trivariate inversion strategy is extensively evaluated over a wide distribution of both ambient temperature and laser fluence. For different cases ranging from room temperature to flame temperature, numerical tests show that the inversion results of all three variables exhibit significantly high accuracy and low uncertainty with proper selection of laser fluence. Furthermore, our study shows that the trivariate inversion strategy can maintain robust performance in ambient temperature fluctuation caused by spatial inhomogeneity of the flame. All these results indicate that this trivariate inversion strategy has a promising application in combustion scenarios.

The Influence of Different Aggregates on the Physico-Mechanical Performance of Alkali-Activated Geopolymer Composites Produced Using Romanian Fly Ash.

In light of the urgent need to develop environmentally friendly materials that, at some point, will allow the reduction of concrete and, consequently, cement consumption-while at the same time allowing the reuse of waste and industrial by-products-alkali-activated fly ash (AAFA) geopolymer composite emerges as a material of great interest. The aim of this study was to investigate the physico-mechanical performance of composites based on AAFA binders and the effect of different types of aggregates on these properties. The experimental results indicate variations in flexural and compressive strength, which are influenced both by the nature and particle size distribution of aggregates and the binder-to-aggregate ratio. The analysis of the samples highlighted changes in porosity, both in distribution and pore size, depending on the nature of the aggregates. This supports the evolution of physico-mechanical performance indicators.

Evaluation of the effects of long‐term maize–peanut intercropping on soil aggregate stability based on different methods

AbstractIntercropping is an effective measure to increase crop yield and improve soil structure. The soil aggregate is the basic unit of soil structure, and its stability is affected by intercropping system. To study the effects of intercropping system on soil aggregate stability, the particle size distribution and stability characteristics of soil aggregates under three planting patterns of maize only (SM), peanut only (SP) and maize intercropped with peanut (M‐P) were evaluated by dry sieving (mainly used to determine the mechanical stability of soil aggregates), wet sieving (mainly used to determine the water stability of soil aggregates) and Le Bissonnais (mainly used to distinguish different decomposition mechanisms of soil aggregates) (including slow wetting (SW), wet stirring (WS) and fast wetting (FW) tests) methods. The results showed that the particle size distribution of soil aggregates was mainly the >2 mm fraction in the dry sieving test, the 1–0.5 mm and <0.053 mm fractions in the wet sieving test, the >2 mm fraction in the SW test, the >2 mm and 2–1 mm fractions in the WS test and the 1–0.5 mm and 0.5–0.25 mm fractions in the FW test. The mean weight diameter (MWD), geometric mean diameter (GMD) and R0.25 of soil aggregates in the three determination methods were all ranked as SP < SM < M‐P, indicating that intercropping could increase the proportion of large aggregates and improve the stability of soil aggregates. In the three tests using the Le Bissonnais method, the MWD order was FW < WS < SW, indicating that the soil aggregates in this area were the most sensitive to slaking effects and the least sensitive to mechanical breakdown, and intercropping could reduce the sensitivity of soil aggregates to slaking effects and mechanical breakdown. In addition, the MWD of dry sieving was significantly positively correlated with the MWD of SW and WS, and the MWD of wet sieving was significantly positively correlated with the MWD of FW. The results of this study could provide a reference for better understanding of farmland soil structure and aggregate stability under intercropping system in the North China Plain.

Evaluation of Grain Size Distribution by Digital Image Processing

Sieve analysis tests are frequently used to determine the grain size distribution of granular materials. This project proposes an ImageJbased image analysis approach for evaluating aggregate particle size distribution. Grain size in image analysis should be estimated to compare the graduation curves between the two methods.. Black sheets were more effective than white sheets for particle placement, perhaps due to light effects. This technology may be utilized for in-suit testing, as it requires a camera and computer. The study used monochromatic light and a highdefinition camera to capture grain photos while controlling for background, light direction, and intensity. A ground truth was established to evaluate errors in determining grain areas. All grain shape parameters are obtained using the ImageJ program. The grain size distribution curve is generated using image analysis.

Impact of typical land use type on the stability and content of carbon and nitrogen of soil aggregates in western Hubei

AbstractIn order to investigate the characteristics of soil structural stability and the factors influencing it under typical land use types in the karst region of western Hubei, soil samples were collected from five land use types (natural mixed forest [NF], cypress forest [CF], stone dike terrace [ST], stone dike forest [SF], and abandoned land [AL]) in Xialaoxi to analyze the particle size distribution of aggregates, structural stability, and the distribution characteristics of carbon, nitrogen, and extracellular enzyme contents. The results showed that the aggregates of NF, CF, SF, and AL were dominated by >2‐mm aggregates, while ST showed mostly 2–0.25‐mm aggregates. The mean weight diameter (EMWD) and the geometric mean diameter (EGMD) of mechanical aggregates showed a trend of CF > NF > SF > AL > ST, whereas the erodibility factor K showed an opposite trend. The total organic carbon (TOC), labile organic carbon (LOC), and total nitrogen (TN) contents of aggregates of each particle size were significantly higher in NF and CF than in ST and AL under the five land types. The content of available nitrogen (AN) was highest in NF and ST. The content of carbon and nitrogen and extracellular hydrolases (β‐glucosidase [BG], cellulose disaccharide hydrolase [CBH], β‐N‐acetyl‐aminoglucosidase [NAG], and leucine aminopeptidase [LAP]) in soil aggregates were mainly concentrated in <0.25–0.053‐mm aggregates. The variation in carbon and nitrogen abundance of each particle size agglomerate mainly originated from the LAP content of that particle size agglomerate and showed significant correlation. The partial least squares path models (PLS‐PM) showed that the main influences on the stability of soil aggregates in small watersheds were land use types, soil physical properties, carbon and nitrogen abundance, and extracellular enzyme activity. In conclusion, the structural stability of soil aggregates in Xialaoxi watershed is the result of the interaction between human disturbance and soil nutrient self‐cycling. In general, NF and CF have better soil aggregate stability and are conducive to soil carbon and nitrogen accumulation, whereas for later development and management of SF, ST, and AL sample plots, scientific cultivation means should be adopted, and attention should be paid to long‐term carbon and nitrogen accumulation and conservation.

Effects of straw-returning combined with application of microbial inoculants on soil aggregates and related nutrients

We analyzed the particle size distribution of soil aggregates in 0-20 and 20-40 cm soil layers of rice-wheat rotation field based on a field plot test with two treatments, conventional straw returning (CK) and straw returning with the addition of straw decomposition promoting microbial inoculants (IT). We evaluated the water stability indices of soil aggregates (the number of soil water stable large aggregates R0.25, the average weight diameter MWD, and the geometric average diameter GMD), and measured the contents of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) in the soil aggregates of ＜0.053, 0.053-0.25, 0.25-1, ＞1 mm. The results showed that: 1) The number of aggregates ＜0.053, 0.053-0.25, ＞0.25 mm in the 0-20 and 20-40 cm soil layers under IT decreased by 10.0% and 6.8%, increased by 3.0% and 5.7%, and 17.9% and 26.1% compared with CK, respectively. IT effectively increased R0.25, MWD, and GMD by 26.4%, 20.0%, 18.2% and 18.2%, 10.5%, 10.0% in 0-20 and 20-40 cm soil, respectively. 2) Compared to CK, the TP content of 0.25-1 mm aggregates in 0-20 and 20-40 cm soil under IT was significantly increased by 40.3% and 37.5%, respectively, without difference in TN and SOC contents. There was no significant difference in nutrient contents of the other aggregates between the treatments. The contents of SOC and TN in large aggregates (>0.25 mm) were higher than those in silty aggregates (<0.053 mm). Compared to CK, the cumulative contribution rates of SOC, TN and TP of <0.053 mm aggregates under IT were decreased in two soil layers. There was no significant difference in the nutrient cumulative contribution rates of 0.053-0.25 mm aggregates between treatments. The cumulative contribution rates of SOC, TN, and TP of large aggregates (>0.25 mm) under IT were 32.1%, 19.6%, 52.8% and 22.8%, 11.8%, 42.9% higher than those under CK in 0-20 and 20-40 cm soils, respectively. 3) The number of <0.053 mm aggregates was significantly negatively correlated with SOC and TP contents, while that of 0.053-0.25 mm aggregates was negatively correlated with nutrient content. The number of large aggregates (>0.25 mm) were significantly positively correlated with SOC, TN, and TP contents. In conclusion, straw returning with microbial-inoculant addition could promote the formation of soil macroaggregates (>0.25 mm), and improve the water stability of soil aggregates, increasing nutrient contents in soil macroaggregates, with the nutrients transferring from silty aggregates to macroaggregates.

Mathematical Modeling of Technological Processes of Concreting Monolithic Structures From Fine-Grained Mixtures

Introduction. The introduction of innovative technologies and materials in the construction industry is constrained by a number of reasons associated with insufficient knowledge of technological processes and the increased cost of highly efficient innovative materials. Therefore, studies in the field of monolithic concrete technology, reflecting the features of concreting structures from self-compacting mixtures and aimed at reducing their cost due to the use of construction waste, are relevant. The purpose of this work is experimental and statistical modeling of rheological characteristics of finegrained self-compacting concrete mixtures and physical and mechanical properties of concretes depending on the influence of two recipe factors — consumption of superplastifying additive and particle size distribution of aggregate including construction wastes.Materials and Methods. For the preparation of fine-grained self-compacting concrete mixtures, portlandcement CEM 0 52,5N, natural quartz sand and crushed concrete sand of a mixture of three fractions of 0.63–5.0 mm and a chemical additive - a superplasticizer based on polycarboxylate esters Polyplast PK were used. Process characteristics of fine-grained self-compacting mixtures (workability, viscosity, fluidity) were determined by standard methods. The shear limits of the mixtures were set by means of an instrument comprising a cylinder with a nozzle and a glass base with circumferential marking. Modeling of rheological and physical-mechanical properties of fine-grained self-compacting concretes was carried out using a two-factor simplex-summed plan on a hexagon inscribed in a circle, which is one of the most convenient for solving technological problems of construction materials science.Results. Experimental-statistical models of rheological characteristics of fine-grained self-compacting mixtures and strength properties of concrete were obtained, adequately describing experimental data.Discussion and Conclusion. The use of mathematical planning methods of the experiment made it possible to comprehensively assess the influence of the two most significant recipe factors on the technological processes of concreting monolithic reinforced concrete structures from fine-grained self-compacting mixtures using aggregate from construction waste. It was established that optimal content of grains from crushed concrete in natural sand is 30–35 %, and dosage of superplasticizer Polyplast PK is 1.2–1.25 % of binder weight.

Straw Inputs Improve Soil Hydrophobicity and Enhance Organic Carbon Mineralization

The mechanism of the influence of soil water repellency (SWR) and agglomeration stability on soil organic carbon (SOC) mineralization has not been thoroughly studied following different methods of returning straw to the field. The research background in this study was ordinary black soil, and the addition of straw was accomplished via straw mixing (CT), straw mulching (CM), straw deep burying (CD), and straw tripling deep burial (CE). A 120-day long-term incubation test was used to measure the contact angle between water droplets and soil, the particle size distribution of aggregates and their organic carbon (OC) content, organic carbon pool (OCP) content, OC contribution, and soil CO2-C release, the extent of SWR and the direct effect of agglomerates on SOC mineralization were assessed under different straw return methods. The results revealed that the water-droplet–soil contact angle (CA) was much greater and the rate of CA decline was significantly lower in the CD treatment compared to the CT, CM, and CE treatments, the rate of water droplet penetration on the soil surface was slower, and the SWR was improved. The CD treatment significantly increased the content of macroaggregates and their OCP content, and also significantly increased the content of microaggregates’ OC. The CO2-C emission rate and cumulative emissions were enhanced by adding the same amount of straw, with the most significant enhancement in the deep straw treatment. The cumulative CO2-C emission rate and SOC mineralization significantly increased with increases in SWR, macroaggregates content, and microaggregates OC content, but significantly decreased with increases in macroaggregates’ OC content, according to principal component analysis and Pearson’s correlation analysis. These results highlight the extent of SWR and the direct effect of agglomerate particle size distribution and OC content on SOC mineralization under different straw return methods. This will help to consolidate soil structural stability and nutrient management to support productivity and SOC sequestration in different agricultural systems.

Calibration of Micro-Parameters of a Mortar Cylinder Specimen under Simple Compression Using a 2D Discrete Element Model

Masonry systems have been used extensively in historical, commercial, and residential buildings. A large number of experimental and computational studies have been conducted to investigate the behavior of masonry components and systems, including mortar, units (e.g., blocks), and walls. The Discrete Element Method (DEM) has been used to analyze masonry systems with a macro modeling methodology (i.e., structural systems like walls). Masonry systems and their components have not been analyzed using a micro-modeling methodology with the DEM. The objective of this paper is the deterministic calibration of micro-parameters of the mortar cylinder model based on a computationally efficient DEM model. To achieve this objective, a parametric analysis is introduced through a series of models of a mortar specimen tested under simple compression to explore the impact of the model micro-parameters when trying to reproduce a set of experimental observations conducted at the Universidad Autonoma de Yucatan Mexico (UADY). A calibration process based on optimization is implemented to determine the best estimates of the model’s micro-parameters. This paper is divided into three analyses. First, the particle size distribution of the mortar’s aggregate is used as a reference (i.e., scale 1), and then up-scaled 1.5 and two times using four particle sizes; second, using the two-times scaled particle size, the influence of varying particle sizes within a reference particle size distribution was explored (from one particle size to 4 particle sizes following the aggregate particle size distribution); and third, a parametric analysis is performed varying seven micro-parameters, one at the time, varying from 0.25 to 1.5, at 0.25 scale increments, on a model including four particle sizes. The results show micro-parameters and stress-strain curves of mortar for the different analyses, and a representation of the cross sections of the models, indicating the distributions of contact forces. All proposed models showed good agreement with the experimental observations (i.e., stress-strain curve). Also, it was observed from the proposed analyses that some micro-parameters vary as the particle size and the scaled particle size distributions change when using the same experimental stress-strain curve. Also, it was found that the proposed DEM must contain at least two particle sizes to significantly improve the particle interlocking to ensure that the mechanistic behavior reproduces the same experimental observations. Finally, from the results presented in this work, it is concluded that it is possible to produce a computationally efficient model that can later serve as a reference for future research accounting for other control variables such as particle shape, particle size distributions, the exploration of damage propagation effects, and most importantly their corresponding uncertainty quantification and propagation effects in masonry systems.

Sustainable aggregate impact on pervious concrete abrasion resistance

Pervious concrete, known for its high porosity and permeability, is commonly used for low-traffic pavements. This study investigates the impact of sustainable aggregates on abrasion resistance in pervious concrete, specifically electric furnace slag (EFS), ceramic construction waste (CW), and concrete block waste (CBW). Therefore, an experimental program was conceived to assess the abrasion resistance through Cantabro test and to evaluate the density, porosity, permeability, and mechanical performance (compressive and flexural tensile strengths) of pervious concrete. Experimental results indicate that CW and CBW aggregates yield similar abrasion resistance (38% and 36% mass loss) to reference aggregates (38%), while EFS aggregates show significantly higher mass loss (73%). The proportion of fines within the particle size distribution of the sustainable aggregate emerges as a critical factor that influences abrasion resistance. This finding underscores the significance of particle size distribution in enhancing abrasion durability. Furthermore, pervious concrete with sustainable aggregates may display favorable abrasion resistance without compromising hydraulic performance. The study contributes to understanding abrasion resistance in pervious concrete and offers insights into sustainable construction materials.

Investigating mechanical properties of cemented gangue backfill materials subjected to static-dynamic combined loads

This study aims to investigate the mechanical properties and failure mechanisms for the cemented gangue backfill materials (CGBM) of the roadside filling structures along the gob-side entry with no coal pillars under static-dynamic combined loading. Cuboid cemented gangue backfill samples (CGBS) with dimensions of 50 × 50 × 100 mm were first prepared, with the fractal dimension D = 2.6 characterizing the particle size distribution of aggregates, and then conducted static-dynamic combined loading tests in terms of various pre-static loads (5–11 MPa), amplitudes (2–5 MPa), frequencies (1–16 Hz) and dynamic loading cycles (20–320 cycles). The test results indicate significant differences in the mechanical properties, acoustic emission (AE) responses, maximum principal strain fields, and ultimate failure modes of CGBS under different static-dynamic combined loading paths. With an increasing pre-static load, dynamic loading amplitude and number of cycles, the peak stress decreases by 21.28 %, 12.48 % and 11.84 % respectively, while the peak stress first decreases and then increases with the dynamic loading frequency and reaches the minimum value at the “characteristic frequency” of 4 Hz. The irreversible strain generally experiences an “acceleration - constant speed” variation during the dynamic loading as a result of the gradually stable damage effects. The dynamic elastic modulus increases by 45.84% in the pre-static load range of 5–11 MPa, while decreases with the dynamic loading amplitude. The dissipated energy increases by a factor of 6.5 with the dynamic loading amplitude, and by 27.52% with the pre-static loads. The significantly stimulated cracking activities at the beginning of dynamic loading result in extremely violent AE responses, and then the AE signals weaken due to the dominate role of opening and closure of existing cracks. The RA and AF calculation results illustrate that shear cracks become active at the initial dynamic loading and macroscopic failure stages. As the degradation effects of dynamic loading intensify, three various failure modes of inclined shear failure, “Y” shaped shear failure and conjugate shear failure for the CGBS are respectively classified, especially the conjugate shear failure characterized by two cone-shaped blocks. The findings will contribute to a better understanding of the mechanical responses and failure mechanisms of the roadside filling body during the dynamic mining.

Afforestation Influences Soil Aggregate Stability by Regulating Aggregate Transformation in Karst Rocky Desertification Areas

Surface vegetation has a substantial impact on soil aggregate stability, which is an important indicator of soil quality. However, there is still limited research on the response of soil aggregate stability indicators and the organic carbon, total nitrogen, and total phosphorus content in soil aggregates for different vegetation patterns in rocky desertification fragile ecological areas. Therefore, in order to study the effects of different vegetation restoration models on soil aggregate stability and aggregate related nutrient content and their promoting relationships in the karst rocky desertification areas in southwest China, soil samples under three artificial restoration vegetation measures (Juglans regia L.-Rosa roxburghii Tratt., Rosa roxburghii Tratt.-Lolium perenne L., Juglans regia L.-Lolium perenne L.) were collected in 0–10 cm and 10–20 cm soil, and the traditional farmland (Zea mays L.) was used as the control, combined with dry and wet sieving experiments for the research and analysis. The results showed that there were significant differences in the distribution of aggregates and soil nutrients among the four types of plots. Compared with traditional agricultural land, artificial afforestation increased the content of soil large macroaggregates (LMAs) and decreased the proportion of microaggregates (MIAs) and silt+clay (SCA), which enhanced the soil aggregate stability and reduced the soil fragmentation and erodibility. The afforestation restoration increased the content of soil aggregate-related SOC, TN, and TP, and increased with the decrease in the aggregate particle size. Research has found that soil aggregate stability indicators are significantly influenced by the particle size distribution of soil aggregates. In the positive succession process of vegetation types, soil nutrient accumulation is controlled by changes in the soil aggregate particle size, which affects the soil aggregate stability and reduces soil erodibility, thereby protecting the soil nutrient loss. The composite management of forest and irrigation in degraded ecological areas has certain reference and indicative significance for ecological restoration in rocky desertification areas.

Experimental investigation on grout propagation in poured aggregates for controlling water inrush in tunnels with flowing water

BackgroundIn recent years, the technology of blocking and grouting of has been widely used in the treatment of groundwater inrush in tunnels in China’s mines. Studies have been carried out on the aggregates pouring under hydrodynamic conditions; however, there is lack of studies of the grout propagation in the pouring aggregate mass, which formed in the earlier stage. This paper presents an experimental investigation of grout propagation in poured aggregates with flowing water, which focused on the second stage, i.e., the grouting stage, after the first stage of aggregates pouring in the salvage of an inundated underground tunnel.ResultsIn this work, a visualized tunnel replica with flowing water was adopted to investigate the main effects of sealing efficiency, water pressure variation, propagation mechanism during grouting in the poured aggregates with different grain sizes and flowing water conditions. A series of experiments with orthogonal array design were carried out, and the propagation of cement/sodium silicate and the change characteristics of fluid pressure in the tunnel were obtained. The results show that the main effects on the sealing efficiency of grouting in poured aggregates with flowing water in a descending order is cement/sodium silicate ratio, relative density of aggregates, particle size distribution of aggregates, and final water pressure difference of both ends of aggregates segment. The cementation form of grouted aggregates has four types; T shape, H shape, O shape and shell shape. The main mode of grout propagation includes three types; permeation filling mode, compaction- permeation mode, and compaction-fracturing mode, which reflects the influence of grain size, density of aggregates and grouting pressure. The variation of water pressure can be divided into four categories; overall ascending type, step ascending type, concave type, and convex type. The curve type mainly depends on water pressure, water flowrate, relative density and grain size of aggregates, gel time, propagation mechanism of grout.ConclusionThe flow rate after grouting has decreased by approximately 60% to 86% compared to that before grouting, bulk hydraulic conductivity decreased by more than 80%, reflecting a great improvement of the sealing efficiency in the grouting stage after the pouring stage. The research on grout propagation in different graded aggregate mass has guiding significance for the design and construction of a rapid disaster treatment after groundwater inrush.

Alleviated environmental constraints and restructured fungal microbiome facilitate aggregate formation and stabilization in coastal mudflat saline soil amended by sewage sludge

AbstractSoil aggregates are important drivers of soil productivity. However, the relative importance of soil abiotic and biotic agents in driving aggregate formation and stabilization remain largely unexplored, especially in coastal mudflat saline soils. We amended saline soil with sewage sludge at rates of 0, 30, 75, and 150 t ha−1 to investigate the effects of sewage sludge on the particle size distribution and stability of soil aggregate and the underlying mechanisms induced by soil environmental factors and fungal community. Results revealed that the sewage sludge amendment significantly (p &lt; 0.05) increased the proportion of macroaggregates with sizes larger than 0.25 mm (R0.25) and enhanced aggregate stability. Moreover, alleviation of saline‐alkali stress and nutrient (C, N, and P) deficiency conditions were significantly (p &lt; 0.05) observed in sewage sludge‐amended soils. Additionally, restructured fungal communities in amended soils harbored significantly (p &lt; 0.05) distinguishable structures and core and unique microbiomes. Particularly, fungal species belonging to Moterella significantly (p &lt; 0.05) enriched in sludge reclaimed soils. Results derived from the random forest (RF) model accompanied by linear regression analysis revealed that soil pH, soil organic carbon, and fungal structural diversity were significantly (p &lt; 0.05) related to aggregate composition (R0.25) and stability (geometric average diameter, GMD). Furthermore, fungal consortia composed of 20 closely interconnected operational taxonomic units (OTUs) affiliated with Ascomycota, Basidiomycota, and Zygomycota explained 28.36% and 49.88% variance of R0.25 and GMD, respectively. Overall, our results revealed the effect of sewage sludge on soil aggregation improvement in coastal areas and highlighted the respective importance of soil chemical properties and fungal microbiome in predicting aggregation status.

Rheological properties of cemented gangue backfill material based on fractal characteristics of waste coal gangue.

The study herein was intended to evaluate the rheological properties of cemented gangue backfill material (CGBM). For this purpose, the rheological test of CGBM with fractal aggregate particle size distribution was carried out, and variations of static yield stress, dynamic yield stress, and plastic viscosity were investigated as a function of fractal dimension and time. The results reveal that aggregate particle size distribution exerts a significant influence upon the rheological properties of CGBM, and with the escalation of the fractal dimension of the aggregate, the yield stress and plastic viscosity initially decline and then increase. In addition, with elapsing time, the correlation between the static yield stress and the fractal dimension of CGBM specimens increases, while the correlations between the dynamic yield stress and the plastic viscosity and the fractal dimension decrease. The relationships between rheological parameters and fractal dimensions at different times are established based on the experimental results. The influence mechanism of aggregate particle size distribution on CGBM is analyzed from the perspective of the aggregate packing state. This study can provide a basis for the ratio design of CGBM in backfill mining.

Improved permafrost stability by revegetation in extremely degraded grassland of the Qinghai-Tibetan Plateau

Revegetation is an effective approach for restoring extremely degraded grassland (DG) in the Qinghai-Tibetan Plateau (QTP). However, little is known about its effects on permafrost stability. Our study investigated changes in the characteristics of DG and revegetated grassland (RG) in alpine permafrost regions of the QTP by means of in situ monitoring and sampling. Compared with DG, soil temperature was lower in warm months and slightly higher in cool months both at 2 and 10 cm depths after revegetation, while soil moisture generally decreased. Revegetation advanced the onset and increased the duration of completely frozen stage. The number of freeze–thaw days decreased at 2 cm but increased at 10 cm depth. The freeze–thaw strength weakened at 2 cm depth in spring and autumn, and at 10 cm depth in autumn, but increased at 10 cm depth in spring. The thawing index at the two depths and active layer thickness in RG were also significantly lower than those in DG. Revegetation significantly affected the particle size distribution and stability of soil aggregates by increasing the proportion of large macroaggregates. Thus, revegetation can effectively improve the permafrost stability of degraded grassland in the QTP and enhance the service functions of alpine grassland ecosystems.

Deep learning from physicochemical information of concrete with an artificial language for property prediction and reaction discovery

Existing machine learning-based approaches to investigate and design concrete mainly use the mixture design variables to predict concrete properties and do not consider the physicochemical properties of ingredients such as the particle size distribution and chemical composition of various binders and aggregates. This paper presents an approach to discover the intrinsic relationships between the physicochemical properties of the ingredients and mechanical properties of concrete. Specifically, this research creates an artificial language to represent concrete mixtures and the physicochemical information of their ingredients, develops a feature extraction method based on character-level N-grams, and proposes a method to configure deep learning models automatically. The proposed approach has been implemented to predict the compressive strength of complex concrete mixtures, assess the importance of variables, and discover chemical reactions, showing high accuracy and high generalizability. This research advances the capabilities of understanding the underlying reactions for complex concrete mixtures and designing low-carbon cost-effective concrete.