Compaction Parameters Research Articles

Predictive Modelling and Functional Group of Clay Soil Treated with Steel Slag and Calcium Carbide Residue

Swell-shrink subgrade soils like clay are rich in sulphate and exhibit a high rate of volume change by swelling and shrinking during wet and dry cycles, respectively, leading to heaves, cracks, and differential settlement of pavement layers. The aim of this study was to establish predictive simulation models and functional group of clay soil treated with steel slag and calcium carbide residue. Physical and geotechnical tests (such as California bearing ratio (CBR), compaction characteristics, and consistency parameters) were carried out on the unstabilised and stabilised clay soil. Using SPSS statistical software (version 23), multiple linear regression analysis was applied to the experimental data that relate CBR to compaction parameters and additive contents (steel slag and calcium carbide residues). The generated models gave a reliable coefficient of determination, R2 , of 0.990, and 0.998, which can be used to successfully predict both unsoaked and soaked CBR of the stabilised clay soil, respectively. Fourier transform infrared spectroscopy (FTIR) analysis was examined on the clay soil, the additives, and the stabilised clay soils. The FTIR analysis showed weak peaks at 1115 and 1103 cm-1 for all the stabilised clay soils, and this revealed that both additives drastically reduced the sulphate content in the clay soil, which consequently reduced the expansion, cracking, and disintegration rates. As a result, the clay soil's chemical bonding was enhanced through physico- chemical mechanisms.

California bearing ratio and compaction parameters prediction using advanced hybrid machine learning methods

California bearing ratio and compaction parameters prediction using advanced hybrid machine learning methods

Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network

Fixed point lattice actions are designed to have continuum classical properties unaffected by discretization effects and reduced lattice artifacts at the quantum level. They provide a possible way to extract continuum physics with coarser lattices, thereby allowing one to circumvent problems with critical slowing down and topological freezing toward the continuum limit. A crucial ingredient for practical applications is to find an accurate and compact parametrization of a fixed point action, since many of its properties are only implicitly defined. Here we use machine learning methods to revisit the question of how to parametrize fixed point actions. In particular, we obtain a fixed point action for four-dimensional SU(3) gauge theory using convolutional neural networks with exact gauge invariance. The large operator space allows us to find superior parametrizations compared to previous studies, a necessary first step for future Monte Carlo simulations and scaling studies. Published by the American Physical Society 2024

Analytical Overview of the Methods of Controlling the Parameters of Vibratory Compaction of the Concrete Mixes

Introduction. A historical (retrospective) overview of the evolution and application of the methods of concrete mixture compaction by vibration, as well as an overview of the breakthrough scientific+ advancements in the studied field have been presented. The use of the dry concrete mixes that reduce cement consumption, accelerate the strength gain of concrete, reduce the shrinkage strain and heat emission and increase the strength and durability of products has been proposed. The theoretical and practical issues of vibratory compaction of the concrete mixes, the research on the effect reached by the various vibration modes of the vibrating tables, the issues of choosing an efficient mode of vibration impact during compacting the concrete mixes and optimisation of their parameters have been studied. The vibrating table design, the installation layout of the vibrator’s eccentric weights to obtain a given value of the exciting force and the layout of a vibration module with the directed vibrations have been studied in detail. The criteria of duration of the concrete mixture compaction have been defined, changes of the impact parameters during compacting the concrete mixes have been characterised and technical specifications of the vibrating tables have been defined. In the article considerable attention has been paid to the main parameters of the vibrocompacting machines: frequency, amplitude and acceleration of the working member. Most often, the parameter of intensity is used to assess the efficiency of the vibration impact received by the mixture during the compaction process.Materials and Methods. A mixture that turns into concrete after compaction, setting and hardening was selected for the research. The various methods and parameters of concrete mixture compaction to obtain the high-quality concrete were studied. The impact of the various vibration modes on the process of compaction of concrete mixes was investigated, the criteria for the efficient selection and optimisation of the vibration impact parameters were revealed. The paper presents the criteria for assessing duration of the concrete mixture compaction and describes the changes in the parameters during compaction of the concrete mixes. The technical specifications of the vibrating tables of different types and sizes were also presented. The choice of the vibration compaction mode for the concrete mixture is a complicated task, which includes many parameters.Results. Upon the research, the data on the process of concrete mixture compaction under the impact of vibration forces has been obtained. The process results in the destruction of the structural bonds and weakening the bonds between the particles, which leads to the emergence of a variable stress-strain condition. It has also been found that under such settings, the mineral particles get transferred and a denser packing is formed.Discussion and Conclusion. The resulting denser packing of the concrete mixture particles ensures the undoubted economic advantage due to reduced consumption of a binder without loss of the strength properties of concrete.

Empirical Correlation of Hossaena Town Soil Index Properties with California Bearing Ratio (CBR) Values

California bearing ratio (CBR) is a key input factor in pavement structural design that is used to determine character size, the strength of unbound materials, and subgrade soils. The construction of a straightforward and logical regression model to forecast the CBR of unbound materials as a function of the index properties of the material. This work explains the relationship between compaction parameters and Atterberg limit tests. Laboratory test was conducted to get the independent (percent finer, liquid limit, plastic limit, plastic index, maximum dry density and moisture content), and dependent variables (CBR). A statistical package for social science software (SPSS) used to develop the mathematical model both in single and multiple regression. The viability of using multiple linear regression analysis to relate CBR values to soil index features was investigated. In this study, SPSS is used to examine each independent variable's significance individually. The multiple linear regression analysis was used to create the correlation, which had an eighteen-person sample size and a modest determination coefficient of R2 = 0.821. The CBR has good relation with maximum dry density and optimum moisture content compared to other parameters. This model applicable for small structure in the specified soil type, MH.

Fabrication of High-Performance Asphalt Mixture Using Waterborne Epoxy-Acrylate Resin Modified Emulsified Asphalt (WEREA)

Existing research shows that using waterborne epoxy resin (WER) instead of emulsified asphalt as the binder for cold mix asphalt (CMA) can enhance the rutting resistance, high-temperature performance, fracture performance, and early performance of CMA. In order to eliminate the potential drawbacks such as insufficient strength and low-temperature performance of CMA during application, a novel method was proposed in this study for the preparation of waterborne epoxy-acrylate resin (WER), specifically tailored to modify emulsified asphalt, resulting in waterborne epoxy-acrylate resin emulsified asphalt (WEREA). The modification effect of WER on emulsified asphalt was evaluated through rheological tests and direct tensile tests. A modified design method based on the conventional Marshall design method was proposed to determine the optimal mix proportions, including the key parameters of specimen compaction and curing. The results revealed that the incorporation of WER led to a substantial improvement in the complex shear modulus and a concurrent decrease in the phase angle. When the temperature exceeded 60 °C, the phase angle exhibited a diminishing trend, indicative of a reduced viscosity as temperatures escalated. As the WER content increased, a decrease in the direct tensile strain rate was observed, accompanied by a substantial elevation in direct tensile strength. At various stress levels, the shear strain of WEREA decreases with increased content of WER, indicating that the incorporation of WER can enhance the hardness of emulsified asphalt and improve its deformation resistance. The results from MSCR tests indicate that WER could significantly improve the elasticity and hardness of emulsified asphalt, transitioning it from a viscoelastic material to an elastic material, thereby improving its deformation resistance, resistance to rutting, and high-temperature performance. The results of fatigue life are consistent with those of the amplitude sweep, both reflecting the improvement of resistance to deformation of emulsified asphalt by WER. This indicates that WER has a significant improving effect on the fatigue resistance of emulsified asphalt. Furthermore, the Marshall design tests further confirmed the advantages of WEREA in asphalt mixtures. The optimal preparation for the WEREA mixture was proposed as follows: double-sided compaction for 50 times each, aging at 60 °C for 48 h, optimal moisture content of 5.14%, cement content of 2.5%, and emulsion content of 8.4%. The optimal mix proportions identified through these tests yielded asphalt mixtures with significantly improved stability, reduced flow value, and enhanced rutting resistance compared to the hot-mix asphalt mixture (HMA) of AC-16. These findings suggest that WEREA has the potential to significantly enhance the durability and longevity of asphalt pavements. For future applications, it can be explored for use in producing cold recycled asphalt mixtures. In addition to designing the WEREA mixture according to AC-16 gradation, consideration can also be given to using a gradation with a smaller nominal maximum aggregate size for the application in the surface layer or ultra-thin wearing course.

Study on Characteristics of Soil Profile Stability Indexes in Sakita

This research examines the suitability of soil as infrastructure which can help in the classification and identification of its properties for ideal engineering structures. Sakita, an area in Gesse III as a case study, is a virgin land, allocated for residential buildings. The need to investigate the soil profile in the report area prompted these studies as its physical properties are used for classifying its various engineering applications. These properties indicate qualitative behaviors of soil when subjected to various types of loads. Apart from physical/visual observation made of the soil, trial pits were dug to depths of 0.5m, 1.0m, 1.5m, 2.0m, and 2.5m respectively and disturbed soil samples were taken for tests. The disturbed soil samples obtained were analyzed for grading including mechanical sieve analysis, specific gravity, plastic limit, liquid limit, and bulk density tests. The resistances of the cone against penetration, compaction, consolidation, and permeability properties were also assessed. The detected geotechnical properties varied significantly with depth, except for specific gravity, which did not vary significantly at 0.5 m with depth. Soil samples from all pits consist mainly of poorly sorted gravel and sand with little fineness. They contain a medium- to coarse-grained fraction of sand on average above 85%. The puncture resistance obtained from the cone puncture test ranges from 100 knm2 to 950 knm2. The average safe bearing capacity estimated for the footing using a factor of safety of 3 at a depth of 1 m was not less than 473 km2 anywhere in the study area. The samples from the three locations generally have good compaction parameters, medium to high permeability, and low compressibility. The highest load-bearing capacity is associated with the lateralized basement ceiling. This means that the safety depth for placing infrastructure foundations in the prescribed research area (Sakita) is the depth where it meets the lateralized basement.

Research on a Method for Identification of Peanut Pests and Diseases Based on a Lightweight LSCDNet Model.

Timely and accurate identification of peanut pests and diseases, coupled with effective countermeasures, is pivotal for ensuring high-quality and efficient peanut production. Despite the prevalence of pests and diseases in peanut cultivation, challenges such as minute disease spots, the elusive nature of pests, and intricate environmental conditions often lead to diminished identification accuracy and efficiency. Moreover, continuous monitoring of peanut health in real-world agricultural settings demands solutions that are computationally efficient. Traditional deep learning models often require substantial computational resources, limiting their practical applicability. In response to these challenges, we introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet. LSCDNet preserves only the transition layers to reduce feature map dimensionality, simplifying the model's complexity. The inclusion of a sandglass block bolsters features extraction capabilities, mitigating potential information loss due to dimensionality reduction. Additionally, the incorporation of coordinate attention addresses issues related to positional information loss during feature extraction. Experimental results showcase that LSCDNet achieved impressive metrics with accuracy, precision, recall, and Fl score of 96.67, 98.05, 95.56, and 96.79%, respectively, while maintaining a compact parameter count of merely 0.59 million. When compared with established models such as MobileNetV1, MobileNetV2, NASNetMobile, DenseNet-121, InceptionV3, and X-ception, LSCDNet outperformed with accuracy gains of 2.65, 4.87, 8.71, 5.04, 6.32, and 8.2%, respectively, accompanied by substantially fewer parameters. Lastly, we deployed the LSCDNet model on Raspberry Pi for practical testing and application and achieved an average recognition accuracy of 85.36%, thereby meeting real-world operational requirements.

Static Compaction for Sustainable Geotechnical Solutions: A Comprehensive Study

The objective of this research is to develop an improved uniaxial static compaction method to address the limitations of the traditional Proctor's dynamic approach for soil compaction. This new approach offers reduced labor, enhanced soil density, and increased compactness. The study compares of static soil compaction characteristics with various soil parameters and explores the concept of Equivalent Static Compaction Energy (ESCE). A diverse range of fine-grained soils with varying range of plasticity was investigated, and a significant correlation of compaction parameters attained by static compaction was observed with the corresponding value of static compaction energy, degree of saturation, void ratio, and plastic limit of soil. The research resulted in the creation of constant-energy curves for static compaction, which were compared to dynamic compaction curves from four compaction attempts. From the study, the ESCE corresponding to standard Proctor, reduced standard Proctor, and reduced modified Proctor tests were found to be within the range of 180-340, 155-308, and 532-664 KJ/ m3, respectively. It was also observed for the static compaction method that after reaching the maximum level of compaction, the dry unit weight of the soil specimen remains constant with further increases in compaction energy.

Effects of elevated temperature and treatment duration on selected index and engineering properties of lateritic soils

Lateritic soils are important to various engineering applications, especially in tropical regions, where they serve as key components in road construction, embankments, and other geotechnical structures. This study investigated the combined effects of elevated temperature and heating duration on the index and engineering properties of lateritic soils derived from two distinct parent rocks (charnockite and quartzite) in Ado-Ekiti, Nigeria. Disturbed lateritic soil samples were heated in a furnace at various temperatures (100, 200, 300, and 400 °C) for two designated durations (2 and 3 h). Standard geotechnical tests were conducted to evaluate Atterberg limits, compaction parameters, and unconfined compressive strength. The results showed that elevated temperature led to a decrease in the plastic limit, liquid limit, plasticity index, optimum moisture content (OMC), and UCS for both soils, though the extent varied between the two parent rocks. For instance, at 400 °C for 3 h, the plasticity index decreased by 47.82% for charnockite-derived soil and 55.22% for quartzite-derived soil. At the same temperature for 2 h, it decreased by 38.79% for charnockite-derived soil and 48.92% for quartzite-derived soil. Similarly, UCS decreased by 29.83% for charnockite-derived soil and 25.97% for quartzite-derived soil at 400 °C for 3 h, while at 400 °C for 2 h, UCS decreased by 21.20% for charnockite-derived soil and 23.48% for quartzite-derived soil. Conversely, maximum dry density (MDD) increased with temperature, rising by up to 20.64% for charnockite-derived soil and 14.66% for quartzite-derived soil at 400 °C for 3 h, and by 18.29% for charnockite-derived soil and 12.62% for quartzite-derived soil at 400 °C for 2 h. Longer heating durations (3 h) generally caused more pronounced changes in soil properties compared to shorter durations (2 h). For instance, OMC decreased by 13.41% for charnockite and 14.18% for quartzite at 400 °C for 3 h compared to a 2-h duration. Statistical analyses indicated that temperature had a significant influence on most properties, while the effect of heating duration was less consistent. These findings highlight the need to consider both temperature and heating duration when evaluating the engineering behavior of lateritic soils exposed to elevated temperatures. Practical implications include potential adjustments in construction practices and design parameters for road construction and embankments in regions prone to high temperatures, such as those experiencing frequent forest fires.

Relativistic model for anisotropic compact stars in embedding class-I spacetime

Abstract In the present article, we introduce a completely new regular model for static, spherically symmetric celestial fluid spheres in embedding class I spacetime. In this regard, needfully, we propose a new suitable metric potential e^λ(r) to generate the present model. The various analyses on energy density, pressure, anisotropic factor, mass, compactness parameter, redshift, and energy condition make sure the model is physically viable on the ground of model stars Vela X-1, Cen X-3, SMC X-4, and LMC X-4. The reported solutions also respect the equilibrium state by satisfying the Tolman–Oppenheimer–Volkoff equation and ensure stability by satisfying the causality condition, condition on the adiabatic index, and Harrison-Zeldovich-Novikov condition. The generated M − R graph matches the ranges of masses and radii for the model compact stars. Additionally, this study provides estimates of
the moment of inertia based on the I − M graph.

Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils

Hydraulic conductivity and compaction parameters are the key considerations in selecting lateritic soils for engineering construction. Nevertheless, the complexity and high cost of the required tests have driven many contractors and field engineers to skip them, resulting in a succession of engineering structure failures. To overcome this limitation, this study developed machine learning-based standalone GUI application to predict lateritic soils’ hydraulic conductivity (K), maximum dry density (MDD) and optimum moisture content (OMC) from indices including specific gravity, liquid limit, plasticity index, linear shrinkage and fine content. To achieve this goal, the geotechnical properties of three hundred samples, collected using grid sampling method from thirty different lateritic deposits in southwestern Nigeria, were evaluated through laboratory tests. The test results were used to train predictive models using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and Gaussian process regression (GPR). The models’ performance was compared using coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). Based on these performance metrics, ANN demonstrated the best performance (R2 = 0.9835, 0.9797, 0.9999; RMSE = 7.938, 0.252, 2.09E-08; MAPE = 0.288, 1.114, 1.587; MAE = 5.432, 0.169, 1.1E-08) for MDD, OMC and K, respectively, followed by GPR and then ANFIS. Thus, the ANN models were selected and embedded in a standalone GUI application to enhance easy and quick prediction of lateritic soils’ MDD, OMC and K. The validity of the ANN-based standalone GUI application was demonstrated by comparing it favorably to notable regression-based models in the literature.

A New Porosity Evaluation Method Based on a Statistical Methodology for Granular Material: A Case Study in Construction Sand

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable.

Adding compact parameter blocks to multimodal transformers to detect harmful memes

Harmful content serves as a potent tool for spreading destructive ideologies and creating divisions among people, often exploiting factors like race, religion, and socioeconomic status. Social media platforms are especially conducive to such content, mainly through memes. Hence, the development of tools that can detect and eliminate harmful memes before they reach a wide audience and cause harm is a very important issue. However, this task represents a critical challenge, as memes can take various forms, typically combining images with text. Recent research has focused on building multimodal machine learning models, especially transformers-based models, to uncover harmful content within memes. Although these models have shown effectiveness, they often require substantial computational resources for training and incorporate additional information besides the textual and visual components of the memes, making them complex and challenging to reproduce. This study introduces a novel approach to enhance the performance of established multimodal transformer models without needing extra information. It works by adding a specific module called Compact Parameter Blocks (CPB) into the encoder segments of these models. This module enhances the quality of input data, leading to improvements in the model’s attention mechanism and in its overall regularization. This results in better performance across all relevant metrics. In this paper, we use this approach with two variations of multimodal models — VisualBERT (Visual Bidirectional Encoder Representations from Transformers) and VilBERT (Vision and Language Bidirectional Encoder Representations from Transformers) — both built on canonical transformers to identify harmful content in memes across four different datasets. The experimental results demonstrated that this method outperforms several robust and complex approaches.

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Investigating the Impact of Ground Granulated Blast Furnace Slag (GGBS) and Cement Kiln Dust (CKD) on fine grained Soil Subgrade in Al Hillah City

Abstract In many parts of the world, fine grained soils are regarded as one of the major concerns for civil engineering projects. To mitigate these problems, fine grained soils are typically replaced with stronger materials. However, due to the high cost of this approach, various researchers have tried alternative approaches, the most popular of which is soil stabilization. This method could be deemed suitable for pavements. The research focuses on stabilizing fine soil through the utilization of cement kiln dust (CKD) and ground granulated blast slag (GGBS). This research involves treating fine soil with 10% of the total binder consist from (GGBS+CKD), with the amount of GGBS to CKD determined by dry soil weight as follows: (10% GGBS + 0% CKD), (8% GGBS + 2% CKD), (6% GGBS + 4% CKD), (2% GGBS + 8% CKD), and (0% GGBS + 10% CKD). The compaction parameters, consistency limitations, and the findings of the Unconfined Compressive Strength (UCS) test were used to assess the enhancement levels. In addition, specimens were subjected to UCS testing after (7-28) days of curing. The findings demonstrated that the combination of GGBS activated by CKD notably enhanced the physical characteristics of fine soil. The Plasticity Index (PI) decreased from 7.4 for untreated soil to 4.8 for a mixture containing 10% binder, comprised of 2% GGBS and 8% CKD, and to 4.25 for a single-binder mixture with 10% CKD. Furthermore, the unconfined compressive strength test (UCS) results indicated that a 10% binder mixture of 8% GGBS and 2% CKD increased the UCS by 2.9 to 5.9 times compared to untreated soil after 7-28 days of curing.

Geotechnical Stability Analysis of the Tiga Dam, Nigeria on the Assessment of Downstream Soil Properties, Erosion Risk, and Seasonal Expansion

The Tiga Dam, a primary hydraulic structure in northern Nigeria, is subjected to intense hydrological stress during the rainy season, posing potential risks to its structural integrity. This study investigates the geotechnical properties and stability of the Tiga Dam in Kano State, Nigeria. Twelve soil samples from the downstream area were analyzed for specific gravity, grain size distribution, Atterberg limits, compaction parameters, permeability, and shear strength. The dam’s stability was assessed using Plaxis 2D under various reservoir conditions. Soil erodibility was evaluated using the Revised Universal Soil Loss Equation (RUSLE), and a linear regression model with noise was developed to predict soil expansion rates. The results showed heterogeneous soil properties, with specific gravity ranging from 2.11 to 2.63 and permeability from 3.40 × 10−9 to 1.49 × 10−7 m/s. Stability analysis revealed factors of safety of 1.322, 1.006, 1.002, and 1.147 for high reservoir, rapid drawdown, slow drawdown, and low reservoir conditions, respectively. The RUSLE K factor ranged from 0.055 to 0.145, indicating low to moderate soil erodibility. The expansion rate model demonstrated high accuracy (R2 = 0.989) in predicting seasonal and long-term soil expansion trends, with peak rates increasing from 16.94 mm/month in 2010–2013 to 19.45 mm/month in 2017–2020. This comprehensive analysis provides crucial insights into the Tiga Dam’s geotechnical behavior, highlighting potential vulnerabilities and the need for targeted management strategies to ensure long-term stability and safety.

Strength behaviour of soil blended with two waste materials-fly ash and tire crumbs with cement

Abstract The rapid development in the economy has led to a growing need for electricity and automobiles, leading to the production of large quantities of fly ash (FA) and discarded tires. The safe disposal of these materials has become a significant problem. FA and scrap tyre derived material has some useful properties which can be beneficially used for enhancing the engineering properties of soil. Therefore, the study investigates the combined impact of FA and scrap tire-derived material on the stress-strain-strength behaviour of a fine-grained residual lateritic soil, incorporating a cementing agent. The research involves compaction tests followed by triaxial compression tests, where FA content added at an increment of 15% starting from 20% by weight of soil. The maximum FA content used in the mix is 50% by weight of soil. Tyre crumb (TC) which is a scrap tire-derived material, is also included in the study, with TC content ranging from 5% to 10% by weight. Additionally, 2% by weight of cement is added to each soil mix as a binding agent. Specimens are compacted in accordance with specific compaction parameters and subsequently cured for duration of up to 28 days. Strength tests are then carried out on those specimens to analyse their strength behaviour. This research primarily examines the shear strength characteristics of soil blended with FA, cement, and TC, emphasizing their geotechnical performance. Addition of 5% TC increases the peak deviator stress of cemented mix having 50% FA content from 1351 kPa to 2710 kPa at 300 kPa confining pressure and 28 days curing period. The inclusion of TC to soil-FA-cement blends is noted to significantly enhance shear strength when compared to mixes without tire crumb. Also, the stress-strain behaviour of soil is significantly influenced by addition of FA and cement in the presence of TC. Therefore, there is an ample scope of utilization of soil-fly ash-cement-TC mixes in geotechnical applications.

The Multifaceted Comparison of Effects of Immobilisation of Waste Imperial Smelting Furnace (ISF) Slag in Calcium Sulfoaluminates (CSA) and a Geopolymer Binder.

Using waste materials as replacements for sand in building materials helps reduce waste and improve the properties and sustainability of the construction materials. Authors proved the possibility of using imperial smelting furnace (ISF) slag granules as a 100% substitute for natural sand in self-compacting (SCC) cement-based mortars of calcium sulfoaluminates (CSA). The study proved that ISF slag's radioactive properties meet this area's requirements. CSA cement eliminates the noted problem in the case of concrete with Portland cement, which is the extended setting of the cement binder. The research findings indicate that using slag to replace sand up to 100% in mortars without grains smaller than 0.125 mm allows high flowability, compaction, low porosity and mechanical parameters. The compressive strength of the CSA cement mortars was about 110 MPa, and more than 140 MPa for geopolymer mortar. Unfortunately, the alkaline pH of a geopolymer causes high leachability of barium and sodium. Thus, the CSA cement is in a more favourable binder to achieve high strength, is environmentally friendly, and is a self-compacting mortar or concrete.

Compaction quality assessment of highway using roller-integrated positive maximum kinetic energy increment detection technique

During conventional highway construction, quality control and acceptance (QC/QA) are based on limited spot tests of material density at random locations which may not comprehensively represent the entire compacted area and are vulnerable to potential biases. Based on roller-integrated positive maximum kinetic energy increment detection technique with real-time kinematic global positioning systems, this research adopted kinetic energy compaction value (KECV) as a real-time monitoring index for the highway compaction quality, and subsequently proposed an KECV-based assessment method to estimate the compaction quality of subgrade and pavement materials. Additionally, a global interpolation method (Green spline) was adopted to obtain estimates for both KECVs and compaction quality at any location on the work area, enabling the analysis of the passing rate of compaction quality for the entire work area. A case study on the Hengyong highway project in China indicates a highly linear correlation between the KECV and the compaction parameters, the compactness of LLL silt, and the relative density of cement-stabilized macadam; therefore, it can serve as a reliable index for monitoring compaction quality. Assessment of compaction quality on the entire work area can be fast and continuously achieved using the proposed method, which can effectively overcome the above limitation of conventional testing, timely feedback compaction information to avoid quality defects, and availably improve the construction quality of highways.