Structural performance of FRP composite bars reinforced rubberized concrete compressive members: Tests and numerical modeling.

Waste tyre rubber has become an environmental and health concern that needs to be sustainably managed to avoid fire hazards and save natural resources. This research work aims to study the structural behavior of glass fiber reinforced polymer (glass-FRP) reinforced rubberized concrete (GRC) compressive elements under monotonic axial compression loads. Nine GRC circular compressive elements with different axial and crosswise reinforcement ratios were fabricated. All the elements were 300 mm in diameter and 1200 mm in height. A 3D nonlinear finite element equation (FEM) was suggested for the GRC compressive elements using a commercial package ABAQUS. A parametric study has been done to examine the effect of various parameters of GRC elements. The test outcomes revealed that the ductility of GRC elements ameliorated with the lessening in the spaces of glass-FRP ties. The addition of rubberized concrete improved the ductility of GRC elements. The damage to GRC elements occurred due to the vertical cracking along the height of the elements. The estimates of FEM were in close agreement with the test outcomes. The suggested empirical equation depending on the 600 test elements, which considered the lateral confinement effect of FRP ties, presented higher accuracy than previous equations.

Effect of banana tree leaves ash as cementitious material on the durability of concrete against sulphate and acid attacks.

The construction industry's rapid growth poses challenges tied to raw material depletion and increased greenhouse gas emissions. To address this, alternative materials like agricultural residues are gaining prominence due to their potential to reduce carbon emissions and waste generation. In this context this research optimizes the use of banana leaves ash as a partial cement substitution, focusing on durability, and identifying the ideal cement-to-ash ratio for sustainable concrete. For this purpose, concrete mixes were prepared with BLA replacing cement partially in different proportions i.e. (0 %, 5 %, 10 %, 15 %, & 20 %) and were analyzed for their physical, mechanical and Durability (Acid and Sulphate resistance) properties. Compressive strength, acid resistance and sulphate resistance testing continued for 90 days with the intervals of 7, 28 and 90 days. The results revealed that up to 10 % incorporation of BLA improved compressive strength by 10 %, while higher BLA proportions (up to 20 %) displayed superior performance in durability tests as compared to the conventional mix. The results reveal the potentials of banana leave ash to refine the concrete matrix by formation of addition C-S-H gel which leads towards a better performance specially in terms of durability aspect. Hence, banana leaf ash (BLA) is an efficient concrete ingredient, particularly up to 10 % of the mix. Beyond this threshold, it's still suitable for applications where extreme strength isn't the primary concern, because there may be a slight reduction in compressive strength.

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector.

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector. Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought. Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model. Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis.

Machine learning approach to predict the biofuel production via biomass gasification and natural gas integrating to develop a low-carbon and environmental-friendly design: Thermodynamic-conceptual assessment.

A hybrid energy cycle (HEC) based on biomass gasification can be suggested as an efficient, modern and low-carbon energy power plant. In the current article, a thermodynamic-conceptual design of a HEC based on biomass and solar energies has been developed in order to generate electric power, heat and hydrogen energy. The planned HEC consists of six main units: two electric energy production units, a heat recovery unit (HRU), a hydrogen energy generation cycle based on water electrolysis, a thermal power generation unit (based on LFR field), and a biofuel production unit (based on biomass gasification process). Conceptual analysis is based on the development of energy, exergy and exergoeconomic assessments. Besides that, the reduction rate of pollutant emission through the planned HEC compared to conventional power plants is presented. In the planned HEC, when hydrogen energy is not needed, excess hydrogen is feed into the combustion chamber to improve system performance and reduce the need for natural gas. Accordingly, the rate of polluting gases emitted from the cycle can be mitigated due to the reduction of fossil fuels consumption. Further, based on the machine learning technique (MLT), the level of biofuel produced from the mentioned process is estimated. In this regard, two algorithms (i.e., Support vector machine and Gaussian process regression) have been employed to develop the prediction model. The findings indicated that the considered HEC can produce about 10.2 MW of electricity, 153 kW of thermal power, and 71.8 kmol/h of hydrogen energy. In both training and testing sets, the Support vector machine model exhibits better behavior compared the two Gaussian process regression model. Based on machine learning technique, with increasing gasification pressure, the level of biofuel obtained from the process does not increase significantly.

Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches.

This study conducted experimental and machine learning (ML) modeling approaches to investigate the impact of using recycled glass powder in cement mortar in an acidic environment. Mortar samples were prepared by partially replacing cement and sand with glass powder at various percentages (from 0% to 15%, in 2.5% increments), which were immersed in a 5% sulphuric acid solution. Compressive strength (CS) tests were conducted before and after the acid attack for each mix. To create ML-based prediction models, such as bagging regressor and random forest, for the CS prediction following the acid attack, the dataset produced through testing methods was utilized. The test results indicated that the CS loss of the cement mortar might be reduced by utilizing glass powder. For maximum resistance to acidic conditions, the optimum proportion of glass powder was noted to be 10% as cement, which restricted the CS loss to 5.54%, and 15% as a sand replacement, which restricted the CS loss to 4.48%, compared to the same mix poured in plain water. The built ML models also agreed well with the test findings and could be utilized to calculate the CS of cementitious composites incorporating glass powder after the acid attack. On the basis of the R2 value (random forest: 0.97 and bagging regressor: 0.96), the variance between tests and forecasted results, and errors assessment, it was found that the performance of both the bagging regressor and random forest models was similarly accurate.

A comprehensive investigation of a water and energy-based waste integrated system: Techno-eco-environmental-sustainability aspects.

In this work, a novel biomass gasifier combined energy system was offered for potable water, heating load, and power generation. The system included a gasifier, an S-CO2 cycle, a combustor, a domestic water heater, and thermal desalination unit. The plant was evaluated from various aspects, i.e., energetic, energetic, exergo-economic, sustainability, and environmental. To this aim, modeling of the suggested system was conducted by EES software; then, a parametric inquiry was carried out to detect the critical performance parameters, considering an environmental impact indicator. The results showed that the freshwater rate, Levelized CO2 emissions, total cost, and sustainability index of 21.19 kg s-1, 0.563 t.MWh-1, 13.13 $.GJ-1, and 1.53 were acquired, each. Moreover, the combustion chamber is a major fount in the irreversibility of the system. Besides, the energetic and exergetic efficiencies were computed at 89.51% and 40.87%. Overall, the offered water and energy-based waste system showed great functionality in terms of thermodynamic, economic, sustainability, and environmental standpoints by enhancing the gasifier temperature.

A hybrid Cycle GAN-based lightweight road perception pipeline for road dataset generation for Urban mobility.

One of the major problems that cause continual trouble in deep learning networks is that training a large network requires massive labelled datasets. The preparation of a massive labelled dataset is a cumbersome task and requires lot of human interventions. This paper proposes a novel generator network 'Sim2Real' transfer is a recent and fast-developing field in machine learning used to bridge the gap between simulated and real data. Training with simulated datasets often converges due to its size but fails to generalize real-world applications. Simulated datasets can be used to train and test deep learning models, enables the development and evaluation of new algorithms and architectures. By simulating road dataset, researchers can generate large amounts of realistic road-traffic dataset that can be used to study and understand several problems such as vehicular object tracking and classification, traffic situation analysis etc. The main advantage of such a transfer algorithm is to use the abundance of a simulated dataset to generate huge realistic-looking datasets to solve data-intense tasks. This work presents a novel, robust sim2real algorithm that converts the labels of a semantic segmentation map to a realistic-looking street view using the Cityscapes dataset and aims to achieve robust urban mobility for smart cities. Further, the generalizability of the Cycle Generative Adversarial Network (CycleGAN) architecture was tested by using an origami robot dataset for sim2real transfer. We show that the results were found to be qualitatively satisfactory for different traffic analysis applications. In addition, road perception was done using a lightweight SVM pipeline and evaluated on the KITTI dataset. We have incorporated Cycle Consistency Loss and Identity Loss as the metrics to evaluate the performance of the proposed Cycle GAN model. We inferred that the proposed Cycle GAN model provides an Identity loss of less than 0.2 in both the Cityscapes dataset and KITTI datasets. Also, we understand that the super-pixel resolution has a good impact on the quantitative results of the proposed Cycle GAN models.

An innovative biomass-driven energy systems for green energy and freshwater production with less CO2 emission: Environmental and technical approaches.

Considering the current crisis of fossil energies, the exploitation of renewables and green technologies is necessary and unavoidable. Additionally, the design and development of integrated energy systems with two or more output products and the maximum usage of thermal losses in order to improve efficiency can boost the yield and acceptability of the energy system. In this regard, this paper develops a comprehensive multi-aspect assessment of the operation of a new solar and biomass energies-driven multigeneration system (MGS). The main units installed in MGS are three electric energy generation units based on a gas turbine process, a solid oxide fuel cell unit (SOFCU) and an organic Rankine cycle unit (ORCU), a biomass energy conversion unit to useful thermal energy, a seawater conversion unit into useable freshwater, a unit for converting water and electricity into hydrogen energy and oxygen gas, a unit for converting solar energy into useful thermal energy (based on Fresnel collector), and a cooling load generation unit. The planned MGS has a novel configuration and layout that has not been considered by researchers recently. The current article is based on presenting a multi-aspect evaluation to study thermodynamic-conceptual, environmental and exergoeconomic analyzes. The outcomes indicated that the planned MGS can produce about 6.31 MW of electrical power and 0.49 MW of thermal power. Furthermore, MGS is able to produce various products such as potable water (∼0.977 kg/s), cooling load (∼0.16 MW), hydrogen energy (∼1.578 g/s) and sanitary water (∼0.957 kg/s). The total thermodynamic indexes were calculated as 78.13% and 47.72%, respectively. Also, the total investment and unit exergy costs were 47.16 USD per hour and 11.07 USD per GJ, respectively. Further, the content of CO2 emitted from the desgined system was equal to 10.59 kmol per MWh. A parametric study has been also developed to identify influencing parameters.

Evaluation of small hydropower turbines installed downstream of Nile River branches (Egypt).

The current study proposes a new strategy for using small hydroelectric turbines in downstream river branches with the least amount of construction and the lowest cost by comparing two different methods of installing the turbines, the first by installing the turbines at the river's bottom and the second by installing the turbines on floating boats. The methodology of this article is based on predicting the distribution of velocities through the watercourse using experimental data collected at various points in the river's depth, and then predicting the resulting electrical power for different sizes of turbines, as well as estimating the number of turbines for each row and the number of rows along the river. Therefore, Investigate the proposed systems. The proposed small hydropower system's economic viability and environmental impact are investigated in this article. According to the nature of the waterway, the best diameter of a turbine that can be used is 1.5 m based on water velocities and river depths. The proposed power plant generated 25.8 kW per single turbine row, with an estimated cost of produced power (0.035 USD/kWh) of approximately 20 turbines installed per row. Compared to other renewable energy sources, the proposed hydropower system is cost-effective and environmentally friendly, as generating electricity with the proposed small hydropower plant could reduce annual carbon dioxide emissions by 368 tones of CO2 per single turbine row.

Optimization of a near-zero-emission energy system for the production of desalinated water and cooling using waste energy of fuel cells.

In the present study, a biomass-based multi-purpose energy system that can generate power, desalinated water, hydrogen, and ammonia is presented. The gasification cycle, gas turbine, Rankine cycle, PEM electrolyzer, ammonia production cycle using the Haber-Bosch process, and MSF water desalination cycle are the primary subsystems of this power plant. On the suggested system, a thorough thermodynamic and thermoeconomic evaluation has been conducted. For the analysis, the system is first modeled and investigated from an energy point of view, after which it is similarly studied from an exergy point of view before the system is subjected to economic analysis (exergoeconomic analysis). The system is evaluated and modeled using artificial intelligence to aid in the system optimization process after energy, exergy, and economic modeling and analysis. The resulting model is then optimized using a genetic algorithm to maximize system efficiency and reduce system expenses. EES software does the first analysis. After that, it sends the data to MATLAB program for optimization and to see how operational factors affect thermodynamic performance and overall cost rate. To find the best solution with the maximum energy efficiency and lowest total cost, multi-objective optimization is used. In order to shorten computation time and speed up optimization, the artificial neural network acts as a middleman in the process. In order to identify the energy system's optimal point, the link between the objective function and the choice factors has been examined. The results show that increasing the flow of biomass enhances efficiency, output, and cost while raising the temperature of the gas turbine's input decreases cost while simultaneously boosting efficiency. Additionally, according to the system's optimization results, the power plant's cost and energy efficiency are 37% and 0.3950$/s, respectively, at the ideal point. The cycle's output is estimated at 18900 kW at this stage.

Basalt Fiber Reinforced Concrete: A Compressive Review on Durability Aspects.

The creation of sustainable composites reinforced with natural fibers has recently drawn the interest of both industrial and academics. Basalt fiber (BF) stands out as the most intriguing among the natural fibers that may be utilized as reinforcement due to their characteristics. Numerous academics have conducted many tests on the strength, durability, temperature, and microstructure characteristics of concrete reinforced with BF and have found promising results. However, because the information is dispersed, readers find it problematic to assess the advantages of BF reinforced concrete, which limits its applications. Therefore, a condensed study that provides the reader with an easy route and summarizes all pertinent information is needed. The purpose of this paper (Part II) is to undertake a compressive assessment of basalt fiber reinforced concrete's durability features. The results show that adding BF significantly increased concrete durability. The review also identifies a research deficiency that must be addressed before BF is used in practice.

Simulation of CO2 capture from natural gas by cyclic pressure swing adsorption process using activated carbon.

This work presented modeling and simulation of CO2 from natural gas. One of the most promising technologies is Pressure Swing Adsorption (PSA), which is an energy-efficient and cost-effective process for separating and capturing CO2 from industrial processes and power plants. This paper provides an overview of the PSA process and its application for CO2 capture, along with a discussion of its advantages, limitations, and future research directions. This process is pressure swing adsorption (PSA) with four adsorption beds. The adsorption bed columns fill with activated carbon as adsorbent. In this simulation momentum, mass and energy balance are solved simultaneously. The process was designed with two beds in adsorption conditions and the other two beds in desorption conditions. The desorption cycle includes blow-down and purge steps. The linear driving force (LDF) estimates the adsorption rate in modeling this process. The extended Langmuir isotherm is used for the equilibrium between solid and gas phases. The temperature changes by heat transfer from the gas phase to solid and axial heat dispersion. The set of partial differential equations is solved using implicit finite difference.

Complex dynamics of induced vortex formation and thermal-fluid coupling in tri-hybrid nanofluid under localized magnetic field: a novel study.

Hybrid nanofluids offer higher stability, synergistic effects, and better heat transfer compared to simple nanofluids. Their higher thermal conductivity, lower viscosity, and interaction with magnetic fields make them ideal for various applications, including materials science, transportation, medical technology, energy, and fundamental physics. The governing partial differential equations are numerically solved by employing a finite volume approach, and the effects of various parameters on the nanofluid flow and thermal characteristics are systematically examined from the simulations based on a self-developed MATLAB code. The parameters included magnetic field strength, the Reynolds number, the nanoparticle volume fraction, and the number and position of the strips in which the magnetic field is localized. It has been noted that the magnetized field induces the spinning of the tri-hybrid nanoparticles, which generates the intricate structure of vortices in the flow. The local skin friction (CfRe) and the Nusselt number (Nu) increase significantly when the magnetic field is intensified. Moreover, adding more nanoparticles in the flow enhances both Nu and CfRe, but with different effects for different nanoparticles. Silver (Ag) shows the highest increase in both Nu (52%) and CfRe (110%), indicating strong thermal-fluid coupling. Alumina (Al2O3) and Titanium Dioxide (TiO2) show lower increases in both Nu (43% and 34%) and CfRe (14% and 10%), indicating weaker coupling in the flow. Finally, compared with the localized one, the uniform magnetic field has a minor effect on the flow and temperature distributions.

Using Artificial Intelligence Techniques to Predict Punching Shear Capacity of Lightweight Concrete Slabs.

Although lightweight concrete is implemented in many mega projects to reduce the cost and improve the project's economic aspect, research studies focus on investigating conventional normal-weight concrete. In addition, the punching shear failure of concrete slabs is dangerous and calls for precise and consistent prediction models. Thus, this current study investigates the prediction of the punching shear strength of lightweight concrete slabs. First, an extensive experimental database for lightweight concrete slabs tested under punching shear loading is gathered. Then, effective parameters are determined by applying the principles of statistical methods, namely, concrete density, columns dimensions, slab effective depth, concrete strength, flexure reinforcement ratio, and steel yield stress. Next, the manuscript presented three artificial intelligence models, which are genetic programming (GP), artificial neural network (ANN) and evolutionary polynomial regression (EPR). In addition, it provided guidance for future design code development, where the importance of each variable on the strength was identified. Moreover, it provided an expression showing the complicated inter-relation between affective variables. The novelty lies in developing three proposed models for the punching capacity of lightweight concrete slabs using three different (AI) techniques capable of accurately predicting the strength compared to the experimental database.

Punching Shear Strength of FRP-Reinforced Concrete Slabs without Shear Reinforcements: A Reliability Assessment.

The recent failure of buildings because of punching shear has alerted researchers to assess the reliability of the punching shear design models. However, most of the current research studies focus on model uncertainty compared to experimentally measured strength, while very limited studies consider the variability of the basic variables included in the model and the experimental measurements. This paper discusses the reliability of FRP-reinforced concrete slabs' existing punching shear models. First, more than 180 specimens were gathered. Second, available design codes and simplified models were selected and used in the calculation. Third, several reliability methods were conducted; therefore, three methods were implemented, including the mean-value first-order second moment (MVFOSM) method, the first-order second moment (FOSM) method, and the second-order reliability method (SORM). A comparison between the three methods showed that the reliability index calculated using the FOSM is quite similar to that using SORM. However, FOSM is simpler than SORM. Finally, the reliability and sensitivity of the existing strength models were assessed. At the same design point, the reliability index varied significantly. For example, the most reliable was the JSCE, with a reliability index value of 4.78, while the Elgendy-a was the least reliable, with a reliability index of 1.03. The model accuracy is the most significant parameter compared to other parameters, where the sensitivity factor varied between 67% and 80%. On the other hand, the column dimension and flexure reinforcement are the least significant parameters compared to other parameters where the sensitivity factor was 0.4% and 0.3%, respectively.

Evaluation of the Strength of Slab-Column Connections with FRPs Using Machine Learning Algorithms.

Slab-column connections with FRPs fail suddenly without warning. Machine learning (ML) models can model the behavior with high precision and reliability. Nineteen ML algorithms were examined and compared. The comparisons showed that the ensembled boosted tree model showed the best, most precise prediction with the highest coefficient of determination (R2) (0.98), the lowest Root Mean Square Error (RMSE) (44.12 kN), and the lowest Mean Absolute Error (MAE) (35.95 kN). The ensembled boosted model had an average of 0.99, a coefficient of variation of 12%, and a lower 95% of 0.97, respectively, in terms of the measured strength. Thus, it was found to be more accurate and consistent compared to all implemented machine learning models and selected traditional models. In addition, the significance of various parameters with respect to the predicted strength was identified, where the effective depth was the most significant by a factor of 0.9, and the concrete compressive strength was the lowest by a factor of 0.3.

A Machine Learning Model for Torsion Strength of Externally Bonded FRP-Reinforced Concrete Beams.

Strengthening of reinforced concrete (RC) beams subjected to significant torsion is an ongoing area of research. In addition, fiber-reinforced polymer (FRP) is the most popular choice as a strengthening material due to its superior properties. Moreover, machine learning models have successfully modeled complex behavior affected by many parameters. This study will introduce a machine learning model for calculating the ultimate torsion strength of concrete beams strengthened using externally bonded (EB) FRP. An experimental dataset from published literature was collected. Available models were outlined. Several machine learning models were developed and evaluated. The best model was the wide neural network, which had the most accurate results with a coefficient of determination, root mean square error, mean average error, an average safety factor, and coefficient of variation values of 0.93, 1.66, 0.98, 1.11, and 45%. It was selected and further compared with the models from the existing literature. The model showed an improved agreement and consistency with the experimental results compared to the available models from the literature. In addition, the effect of each parameter on the strength was identified and discussed. The most dominant input parameter is effective depth, followed by FRP-reinforcement ratio and strengthening scheme, while fiber orientation has proven to have the least effect on the prediction output accuracy.

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning.

In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model's performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study's findings.

Heat-Induced Spalling of Concrete: A Review of the Influencing Factors and Their Importance to the Phenomenon.

Heat-induced spalling in concrete is a problem that has been the subject of intense debate. The research community has, despite all the effort invested in this problem, few certain and definitive answers regarding the causes of and the way in which spalling happens. A major reason for this difficulty is the lack of a unified method for testing, which makes comparing data from various studies against each other a difficult task. Many studies have been performed that show the positive effects of using synthetic micro-fibres, such as polypropylene (PP). The mechanism with which PP fibres improve heat-induced spalling resistance in concrete, however, remains a subject of debate. This paper, therefore, looks at the work that has been performed in the field of spalling (particularly spalling of self-compacting concrete (SCC)). Influencing factors are identified and their links to each other (as reported) are discussed. A particular emphasis is put on discussing the role of PP fibres and how they improve the behaviour of high-performance concrete (HPC) at elevated temperatures. A brief summary of the reviewed papers are provided for each of the influencing factors to help the reader navigate with ease through the references. An introduction to heat-induced spalling and the common causes (as reported in the literature) is also included to highlight the wide range of theories trying to explain the spalling phenomenon.

Overview of Concrete Performance Made with Waste Rubber Tires: A Step toward Sustainable Concrete.

Utilizing scrap tire rubber by incorporating it into concrete is a valuable option. Many researchers are interested in using rubber tire waste in concrete. The possible uses of rubber tires in concrete, however, are dispersed and unclear. Therefore, a compressive analysis is necessary to identify the benefits and drawbacks of rubber tires for concrete performance. For examination, the important areas of concrete freshness, durability, and strength properties were considered. Additionally, several treatments and a microstructure investigation were included. Although it has much promise, there are certain obstacles that prevent it from being used as an aggregate in large numbers, such as the rubber's weak structural strength and poor binding performance with the cement matrix. Rubber, however, exhibits mechanical strength comparable to reference concrete up to 20%. The evaluation also emphasizes the need for new research to advance rubberized concrete for future generations.