Empirical Model Research Articles

Pipeline Corrosion Prediction and Uncertainty Analysis with an Ensemble Bayesian Neural Network Approach

This study developed an ensemble Bayesian Neural Network (BNN) model for pipeline corrosion prediction incorporating uncertainty analysis. The performance of the proposed BNN model was compared with different empirical and data-driven models. Shapley Additive Explanation (SHAP) values were used to quantify the importance of input factors. Results indicate that the ensemble BNN model demonstrates superior prediction accuracy, achieving the highest R2 value of 0.949, with the lowest Mean Absolute Error (MAE) of 0.33mm and Root Mean Square Error (RMSE) of 0.449mm on corrosion depth. It outperforms other models in predicting maximum corrosion depth and uniquely captures uncertainty information, which deterministic models cannot provide. Key factors influencing corrosion include chloride content, pH, pipe-to-soil potential, and pipeline age. Specifically, conditions such as a pipe-to-soil potential exceeding -0.85V, a pipeline age over 20 years, and pH values below 7 are found to significantly accelerate corrosion. The model also illustrates the effects of different influencing factors on corrosion growth uncertainty. Higher chloride content, lower pH values, and higher pipe-to-soil potentials may expand the uncertainty bounds. The proposed model presents the dynamic changes in prediction uncertainty over time, which is usually ignored in previous models. It can improve the precision and reliability of long-term predictions concerning pipeline corrosion depth. This knowledge of corrosion uncertainty is crucial for making informed decisions about pipeline inspection and maintenance strategies.

Exploring opportunities for public sector organisations to connect wellbeing to resource loops in a regional circular economy

ABSTRACT This paper contributes towards our understanding of the role of public sector organisations in delivering circular economy (CE) goals, suggesting that such organisations seek to deliver social value when implementing CE activities. Through empirical evidence from Wales, and a programme designed to deliver circular outcomes via increased collaborative working in public sector organisations, the paper shows that public sector actors interpret the CE as a means to deliver well-being outcomes. The paper shows that public sector organisations, in Wales, are seeking to exploit the maximum social value from CE activities. This, the paper argues, requires a nuanced attention to the context and circumstances in which public service delivery is implemented. A legislative framework, the Wellbeing Future Generations Act (2015), that promotes collaborative working with people and communities to prevent persistent problems such as poverty, health inequalities and climate change, is supporting the creation of a definition of a CE that connects people and wellbeing to resource loops. The key contribution of the paper is to provide an empirical model demonstrating how public sector organisations in Wales are enacting CE in Wales, mediating between and integrating the social, material and spatial elements of a CE.

Rapid Mass Conversion for Environmental Microplastics of Diverse Shapes.

Rivers have been recognized as the primary conveyors of microplastics to the oceans, and seaward transport flux of riverine microplastics is an issue of global attention. However, there is a significant discrepancy in how microplastic concentration is expressed in field occurrence investigations (number concentration) and in mass flux (mass concentration). Of urgent need is to establish efficient conversion models to correlate these two important paradigms. Here, we first established an abundant environmental microplastic dataset and then employed a deep neural residual network (ResNet50) to successfully separate microplastics into fiber, fragment, and pellet shapes with 92.67% accuracy. We also used the circularity (C) parameter to represent the surface shape alteration of pellet-shaped microplastics, which always have a more uneven surface than other shapes. Furthermore, we added thickness information to two-dimensional images, which has been ignored by most prior research because labor-intensive processes were required. Eventually, a set of accurate models for microplastic mass conversion was developed, with absolute estimation errors of 7.1, 3.1, 0.2, and 0.9% for pellet (0.50 ≤ C < 0.75), pellet (0.75 ≤ C ≤ 1.00), fiber, and fragment microplastics, respectively; environmental samples have validated that this set is significantly faster (saves ∼2 h/100 MPs) and less biased (7-fold lower estimation errors) compared to previous empirical models.

Does high-latitude ionospheric electrodynamics exhibit hemispheric mirror symmetry?

Abstract. Ionospheric electrodynamics is a problem of mechanical stress balance mediated by electromagnetic forces. Joule heating (the total rate of frictional heating of thermospheric gases and ionospheric plasma) and ionospheric Hall and Pedersen conductances comprise three of the most basic descriptors of this problem. More than half a century after identification of their central role in ionospheric electrodynamics, several important questions about these quantities, including the degree to which they exhibit hemispheric symmetry under reversal of the sign of dipole tilt and the sign of the y component of the interplanetary magnetic field (so-called “mirror symmetry”), remain unanswered. While global estimates of these key parameters can be obtained by combining existing empirical models, one often encounters some frustrating sources of uncertainty: the measurements from which such models are derived, usually magnetic field and electric field or ion drift measurements, are typically measured separately and do not necessarily align. The models to be combined moreover often use different input parameters, different assumptions about hemispheric symmetry, and/or different coordinate systems. We eliminate these sources of uncertainty in model predictions of electromagnetic work J⋅E (in general not equal to Joule heating ηJ2) and ionospheric conductances by combining two new empirical models of the high-latitude ionospheric electric potential and ionospheric currents that are derived in a mutually consistent fashion: these models do not assume any form of symmetry between the two hemispheres; are based on Apex magnetic coordinates (denoted Apex), spherical harmonics, and the same model input parameters; and are derived exclusively from convection and magnetic field measurements made by the Swarm and CHAMP satellites. The model source code is open source and publicly available. Comparison of high-latitude distributions of electromagnetic work in each hemisphere as functions of dipole tilt and interplanetary magnetic field clock angle indicates that the typical assumption of mirror symmetry is largely justified. Model predictions of ionospheric Hall and Pedersen conductances exhibit a degree of symmetry, but clearly asymmetric responses to dipole tilt and solar wind driving conditions are also identified. The distinction between electromagnetic work and Joule heating allows us to identify where and under what conditions the assumption that the neutral wind corotates with the Earth is not likely to be physically consistent with predicted Hall and Pedersen conductances.

AI based optimal analysis of electro-osmotic peristaltic motion of non-Newtonian fluid with chemical reaction using artificial neural networks and response surface methodology

Purpose The present work is devoted to investigating the sensitivity analysis of the electroosmotic peristaltic motion of non-Newtonian Casson fluid with the effect of the chemical reaction and magnetohydrodynamics through the porous medium. The main focus is on flow efficiency quantities such as pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall. This initiative is to bridge the existing gap in the available literature. Design/methodology/approach The governing equations of the problem are mathematically formulated and subsequently simplified for sensitivity analysis under the assumptions of a long wavelength and a small Reynolds number. The simplified equations take the form of coupled nonlinear differential equations, which are solved using the built-in Matlab routine bvp4c. The response surface methodology and artificial neural networks are used to develop the empirical model for pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall. Findings The empirical model demonstrates an excellent fit with a coefficient of determination reaching 100% for responses, frictional forces on the upper wall and frictional forces on the lower wall and 99.99% for response, for pressure rise per wavelength. It is revealed through the sensitivity analysis that pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall are most sensitive to the permeability parameter at all levels. Originality/value The objective of this study is to use artificial neural networks simulation and analyze the sensitivity of electroosmotic peristaltic motion of non-Newtonian fluid with the effect of chemical reaction.

Enhancement the electrical and linear/nonlinear optical properties of ZnCo2O4 through Al3+doping

In this work, ZnCo2O4 samples doped with Al3+ were prepared by the hydrothermal method. Synchrotron x-ray diffraction, FTIR, SEM and EDX techniques were used to investigate the structure, microstructure, morphology, and composition of ZnCo2-xAlxO4 samples. Rietveld analysis revealed a nearly inverse structure for the undoped ZnCo2O4. The degree of inversion is increased upon increasing the Al3+ doping content. Al ions preferentially occupy the octahedral B-site. The optical band gaps of ZnCo2−xAlxO4 samples are 1.9, 1.8 and 1.91 eV for x = 0, 0.1 and 0.2, respectively. Different empirical models were used to obtain the refractive index of the different samples using their corresponding optical band gaps. The increase in non-linear optical values in the sample with x = 0.1 suggested that this sample could be used in a variety of photonic applications. The effects of frequency and temperature on the dielectric permittivity, impedance and electrical modulus of different samples were explored. All samples exhibited the non-Debye relaxation phenomenon. The relaxation time of each sample is affected with the frequency and temperature. The Cole-Cole curve is employed to reduce uncertainty regarding the electrical characteristics of these ZnCo2−xAlxO4 samples induced by grain boundaries or bulk grain. The substitution of Al3+ ions can regulate the conductivity of the ZnCo2O4 sample.

An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations

We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies. A primary goal of this work is to produce mock galaxy catalogs for the purpose of validating existing models and methods for the mitigation of IA in weak lensing measurements. This technique can also be used to produce new, simulation-based predictions for IA and galaxy clustering. Our model is probabilistically formulated, and rests upon the assumption that the orientations of galaxies exhibit a correlation with their host dark matter (sub)halo orientation or with their position within the halo. We examine the necessary components and phenomenology of such a model by considering the alignments between (sub)halos in a cosmological dark matter only simulation. We then validate this model for a realistic galaxy population in a set of simulations in the Illustris-TNG suite. We create an HOD mock with Illustris-like correlations using our method, constraining the associated IA model parameters, with the between our model’s correlations and those of Illustris matching as closely as 1.4 and 1.1 for orientation–position and orientation–orientation correlation functions, respectively. By modeling the misalignment between galaxies and their host halo, we show that the 3-dimensional two-point position and orientation correlation functions of simulated (sub)halos and galaxies can be accurately reproduced from quasi-linear scales down to . We also find evidence for environmental influence on IA within a halo. Our publicly-available software provides a key component enabling efficient determination of Bayesian posteriors on IA model parameters using observational measurements of galaxy-orientation correlation functions in the highly nonlinear regime.

Factors affecting the adoption of high technology in vegetable production in Hanoi, Vietnam

High-tech vegetable production is becoming a priority in agricultural development in Vietnam in the context of digital economic development. This study aims at identifying factors involving the adoption of high technology in vegetable production by local farmers in Hanoi. We used the theory of planned behavior and other farmers’ personal, social and economic factors to develop empirical model and hypotheses. Primary data was collected from a survey of 450 vegetable producers in Hanoi using cluster sampling method combined with random selection. Then, binary logit model was used to analyze the impact of influencing factors. Results showed that there were 7 factors having significant influences the decision to apply technology in vegetable production of farmers including attitude on high tech production, access to information, size of farm, member of extension organization, education level, access to credit and perceived behavior control, in which attitude variable was the most influential factor. Main management implications raised included enhancing access to technological information, providing demostration visits, giving more extension services, improving social inclusion and implementing hi- tech training for farmers in vegetable production.

An Unmet goal? A Scale Development and Model test of the role of Inclusive Leadership

AbstractThere is a lack of evidence on whether inclusive leadership (IL) truly fosters inclusion for those in historically marginalized groups. Further, a conceptually up-to-date and practically useful measure of IL is lacking. Thus, in two separate phases, we first present a newly developed measure of IL and then test an empirical model. In Phase 1, results from five demographically diverse samples demonstrate reliability and validity evidence of a 10-item IL scale. In Phase 2, use this scale to examine how IL may support members of historically marginalized groups by fostering (a) interpersonal behaviors that uphold inclusive work environments (allyship, antiracism) and (b) individual experiences of inclusion (workgroup inclusion, authenticity). Path analysis results from a multi-wave study support the influence of IL on these outcomes and also demonstrate support for IL’s indirect effects on outcomes that may be disproportionately high (emotional exhaustion) and low (empowerment) for members of marginalized groups. Moderation analyses reveal many of the hypothesized relationships are similar across groups representing marginalized identities on the bases of race, sex, and the intersection of race and sex. These results add evidence to the importance of IL for fostering inclusive workplaces, especially for those who face the greatest barriers to inclusion.

Effect of 2-hydroxyethylammonium carboxylate protic ionic liquids on the solubility and cytotoxicity of indomethacin

Recently, there is a particular interest to utilize protic ionic liquids (PILs) in drug solubility. This study is exploring the effect of three protic ionic liquids (PILs) based on 2-hydroxyethylammonium carboxylate [2-hydroxyethylammonium acetate (MEAA), 2-hydroxyethylammonium lactate (MEAL), and 2-hydroxyethylammonium propionate (MEAP)] on the solubility of the very poorly soluble drug in water, indomethacin (IMC). The shake flask method was used to measure the experimental solubility of IMC at the different temperatures range (298.15–313.15) K. The results demonstrate significantly enhancment the solubility of IMC in PILs compared to pure water, with an approximate increase of 200 times. The experimental solubility data have been correlated using the empirical models which showed the performance as the order: Modified Apelblat–Jouyban–Acree > Van’t Hoff–Jouyban–Acree > Modified Apelblat equations and also the performance for the Wilson model indicated as the order (absolute relative deviation): 2-hydroxyethylammonium acetate (3.030) > 2-hydroxyethylammonium propionate (3.239) > 2-hydroxyethylammonium lactate (7.665). Then the thermodynamic dissolution properties were obtained by usage of Gibbs and Van’t Hoff equations to investigate the thermodynamic behavior of the IMC in the aqueous solution PILs. Eventually, the cytotoxicity of the co-solvents (PILs) under study was evaluated using a standard MTT assay. The results showed that the cell viability percentage increased in the following order: MEAA < MEAP < MEAL. These findings indicated that these PILs had low to moderate toxicity. It is noteworthy that the functional groups of the anions were not the only determinant factor of the cytotoxicity. Other factors encompassing concentration, exposure time, and cell line characteristics also had significant effects.

Study of wellbore instability in shale formation considering the effect of hydration on strength weakening

Shale formations often contain a high proportion of clay minerals, which, upon contact with drilling fluid, undergo hydration expansion. This leads to wellbore instability, a problem that poses significant challenges globally. This study aims to investigate the variation of mechanical properties of shale with respect to hydration time. We employ an empirical model that relates shale strength parameters to the time of drilling through geological formations. Additionally, we consider both shear failure along the wellbore boundary and shear sliding along bedding planes in the analysis. We establish a predictive model for wellbore instability in shale formations. The model quantitatively analyzes the variation of wellbore collapse pressure with drilling time. The research findings indicate that, when the influence of bedding is considered, both the wellbore collapse pressure and the optimal well trajectory undergo significant changes, in addition, for some wellbore trajectories, the collapse pressure can increase by more than 30%. Therefore, it is essential to account for the influence of bedding in wellbore stability analysis in shale formations. As the bedding dip angle changes, both the numerical values and distribution range of wellbore collapse pressure and the optimal well trajectory change noticeably. Changes in bedding dip direction, however, do not affect the numerical values of collapse pressure but do influence the distribution region of the optimal well trajectory. Thus, in wellbore trajectory design within shale formations, it is crucial to determine the orientation of bedding and adjust the well trajectory accordingly to enhance wellbore stability. Furthermore, shale hydration does not impact the optimal well trajectory for a block, but with prolonged hydration, the minimum drilling fluid density required to maintain wellbore stability gradually increases. This suggests that hydration intensifies the weakening effect on bedding plane strength. The research results are helpful to understand the effect of hydration on shale wellbore stability and ensure shale wellbore stability during drilling cycle.

Estimation of potato yield using a semi-mechanistic model developed by proximal remote sensing and environmental variables

Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non-destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that site-specific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, sktmax, and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology.

Empirical models for calculating soil wetting patterns under surface drip irrigation systems: A comprehensive analysis

AbstractAccurate estimation of the surface wetted radius (R) and vertical wetted depth (Z) of wetting patterns in drip irrigation systems is crucial for ensuring that the designs of such systems are effective. This study compared 14 empirical models for estimating drip irrigation wetting patterns by assessing their accuracy using published measurement data and HYDRUS‐2D/3D simulations. The technique for order of preference by similarity to the ideal solution (TOPSIS) was employed to comprehensively rank the models. The results indicate that the empirical model proposed by Fan et al. (2023) (FY) exhibited the highest accuracy when the estimations of R and measured and simulated values were compared, with mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe modelling efficiency (NSE), and percent bias (PB) values of 2.2 cm, 3.4 cm, 0.79, and −7.1% and 5.2 cm, 7.0 cm, 0.50, and −14.1%, respectively. The empirical model proposed by Amin and Ekhmaj (2006) (AE) demonstrated the highest accuracy when the estimations of Z were compared with measured and simulated values, with MAE, RMSE, NSE and PB values of 1.7 cm, 2.0 cm, 0.95 and 4.15% and 4.4 cm, 5.9 cm, 0.82 and 4.7%, respectively. The comprehensive rankings of available models in the present study indicate that the FY model is the most universally applicable, followed by the Li et al. (2022) (LY) model, with comprehensive indices of 0.960 and 0.936, respectively. This research can aid in the selection of universally applicable, reliable and straightforward empirical models for estimating wetting patterns in drip irrigation systems.

Future changes in global rainfall erosivity: Insights from the precipitation changes

Quantifying future changes in rainfall erosivity is essential to address potential water erosion risks. This study reveals a significant power function relationship between rainfall erosivity and precipitation both globally and across various climatic zones. Building on this foundation, new empirical models were developed to project relative changes in rainfall erosivity. These models, predicted on the understanding that General Circulation Models (GCMs) provide more accurate predictions of climate change trends than exact meteorological values, utilizes the ratio of modeled historical precipitation to future precipitation under various scenarios as the variable, formulated as a power function. Data from three GCMs runs from both the Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP6–including historical, mid-term, and long-term periods–were employed. The analysis suggests that by the end of the 21st century, global annual rainfall erosivity could increase by 2.6% under the Shared Socio-Economic Pathway (SSP)1–2.6 scenario, by 3.5% under SSP2-4.5, and by a significant 6.4% under SSP5-8.5, relative to the 2000–2010 baseline. Furthermore, over 76% of the global land area is projected to experience an increase in rainfall erosivity over the century. Regions with projected changes in rainfall erosivity, whether increase or decrease, are likely to face more pronounced changes from mid-century onwards. The CMIP6 exhibits improved model consistency over its predecessor, CMIP5, indicating a greater water erosion risk as global warming progresses. These projections offer insights for strategies to combat soil degradation due to climate changes.

Swarm Optimisation to Model the Surface Roughness of an AISI 4340 Turning using the Hot Machining Process

Given that surface roughness is used to determine product quality, it is a crucial consideration in turning machining. Moreover, it considerably affects the cost of machining. This study forecasts surface roughness values for AISI 304 stainless-steel hot lathe machining using the particle swarm optimisation (PSO) methodology. The workpiece is heated to 100, 150 or 200 degrees Celsius before being turned. Afterwards, the depth, speed and feeding rate of cutting are adjusted to determine the surface roughness of the workpiece. The feeding rate is determined to be the most considerable influence in raising the surface roughness value, followed by cutting depth, cutting speed and workpiece temperature. In terms of accuracy, empirical modelling performs better. The PSO methodology illustrates an effective and straightforward method that can be applied to calibrate different empirical machining models.

Experimental study on the dynamic characteristics of biodiesel/ethanol droplets impacting upon the inclined heated wall

The investigation of droplet impingement plays a crucial role in understanding the dynamics of fuel impact on the walls inside engine combustion chambers. To study the dynamics and heat transfer characteristics of two-component mixed droplets impacting upon an inclined stainless steel heated wall, the effect of wall tilt angle (0°–40°), Weber numbers (We, ranging from 50 to 210), and wall temperatures (Tw, between 25 and 350 °C) on the heat transfer models and spreading diameters is conducted. Through quantitative analysis of high-speed images captured during the experiments, it is observed that the spreading diameter of droplet impact on the inclined wall is closely correlated with both the wall temperature and We of the droplet. The wall tilt angle is found to modify the heat transfer state transition of the droplet to some extent. Additionally, the influence of gravity results in significant differences between the front and back spreading coefficients of the droplet. Based on the available data, a new empirical model is proposed that incorporates temperature terms and inclination to predict the maximum spreading coefficient of droplets impact on an inclined heated wall. Compared with experimental data of this study and in the literature, the maximum prediction error is less than 12.3%.

Bridging 20 Years of Soil Organic Matter Frameworks: Empirical Support, Model Representation, and Next Steps

AbstractIn the past few decades, there has been an evolution in our understanding of soil organic matter (SOM) dynamics from one of inherent biochemical recalcitrance to one deriving from plant‐microbe‐mineral interactions. This shift in understanding has been driven, in part, by influential conceptual frameworks which put forth hypotheses about SOM dynamics. Here, we summarize several focal conceptual frameworks and derive from them six controls related to SOM formation, (de)stabilization, and loss. These include: (a) physical inaccessibility; (b) organo‐mineral and ‐metal stabilization; (c) biodegradability of plant inputs; (d) abiotic environmental factors; (e) biochemical reactivity and diversity; and (f) microbial physiology and morphology. We then review the empirical evidence for these controls, their model representation, and outstanding knowledge gaps. We find relatively strong empirical support and model representation of abiotic environmental factors but disparities between data and models for biochemical reactivity and diversity, organo‐mineral and ‐metal stabilization, and biodegradability of plant inputs, particularly with respect to SOM destabilization for the latter two controls. More empirical research on physical inaccessibility and microbial physiology and morphology is needed to deepen our understanding of these critical SOM controls and improve their model representation. The SOM controls are highly interactive and also present some inconsistencies which may be reconciled by considering methodological limitations or temporal and spatial variation. Future conceptual frameworks must simultaneously refine our understanding of these six SOM controls at various spatial and temporal scales and within a hierarchical structure, while incorporating emerging insights. This will advance our ability to accurately predict SOM dynamics.

Core performance predictions in projected SPARC first-campaign plasmas with nonlinear CGYRO

This work characterizes the core transport physics of SPARC early-campaign plasmas using the PORTALS-CGYRO framework. Empirical modeling of SPARC plasmas with L-mode confinement indicates an ample window of breakeven (Q &amp;gt; 1) without the need of H-mode operation. Extensive modeling of multi-channel (electron energy, ion energy, and electron particle) flux-matched conditions with the nonlinear CGYRO code for turbulent transport coupled to the macroscopic plasma evolution using PORTALS reveals that the maximum fusion performance to be attained will be highly dependent on the near-edge pressure. Stiff core transport conditions are found, particularly when fusion gain approaches unity, and predicted density peaking is found to be in line with empirical databases of particle source-free H-modes. Impurity optimization is identified as a potential avenue to increase fusion performance while enabling core-edge integration. Extensive validation of the quasilinear TGLF model builds confidence in reduced-model predictions. The implications of projecting L-mode performance to high-performance and burning-plasma devices is discussed, together with the importance of predicting edge conditions.

Bond Stress Behavior of a Steel Reinforcing Bar Embedded in Geopolymer Concrete Incorporating Natural and Recycled Aggregates

Abstract: The rise in greenhouse gases, particularly carbon dioxide (CO2) emissions, in the atmosphere is one of the major causes of global warming and climate change. The production of ordinary Portland cement (OPC) emits harmful CO2 gases, which contribute to sporadic heatwaves, rapid melting of glaciers, flash flooding, and food shortages. To address global warming and climate change challenges, this research study explores the use of a cement-less recycled aggregate concrete, a sustainable approach for future constructions. This study uses fly ash, an industrial waste of coal power plants, as a 100% substitute for OPC. Moreover, this research study also uses recycled coarse aggregates (RCAs) as a partial to complete replacement for natural coarse aggregates (NCAs) to preserve natural resources for future generations. In this research investigation, a total of 60 pull-out specimens were prepared to investigate the influence of steel bar diameter (9.5 mm, 12.7 mm, and 19.1 mm), bar embedment length, (4 and 6), and percentage replacements of NCA with RCA (25%, 50%, 75%, and 100%) on the bond stress behavior of cement-less RA concrete. The test results exhibited that the bond stress of cement-less RCA concrete decreased by 6% with increasing steel bar diameter. Moreover, the bond stress decreased by 5.5% with increasing bar embedment length. Furthermore, the bond stress decreased by 7.6%, 7%, 8.8%, and 20.4%, respectively, with increasing percentage replacements (25%, 50%, 75%, and 100%) of NCA with RCA. An empirical model was developed correlating the bond strength to the mean compressive strength of cement-less RCA concrete, which matched well with the experimental test results and predictions of the CEB-FIP model for OPC. The CRAC mixes exhibited higher costs but significantly lower embodied CO2 emissions than OPC concrete.

Pore-scale investigations of permeability of saturated porous media: Pore structure efficiency

Accurate estimation of permeability and the establishment of correlations with pore space properties have garnered attention due to the increasing demand for oil and gas reservoir development as well as geological carbon sequestration. However, prevailing theoretical and empirical models often rely on porosity and pore sizes as key pore space properties to predict permeability, overlooking the variability of pore structure. To delineate the intrinsic associations between permeability and pore space properties, a self-developed visualized experimental system with controllable pore space properties is used to measure permeability of porous media with varying porosity and particle arrangements. Various parameters, encompassing porosity, pore sizes (maximum pore diameter and pore throat diameter) and fractal parameters (pore fractal dimension, succolarity, and lacunarity) are extracted to quantify pore space properties, and further utilized to develop permeability models. Results demonstrate a positive correlation between permeability and porosity, maximum pore diameter, and average pore throat diameter. Additionally, particle arrangements yield pronounced anisotropy in permeability. Relying solely on porosity and pore sizes to portray pore space properties falls short of comprehensively characterizing permeability. Incorporating fractal characterization through metrics like succolarity and lacunarity emerges as a potent avenue for delineating heterogeneity and pore connectivity. This approach proves useful in characterizing the effect of pore structure variability on permeability. The established permeability model rooted in flow direction succolarity and normalized lacunarity aligns effectively with experimental data, capturing the essential physics of permeability variation.