A Comparative Study of Ni-Based Catalysts Prepared by Various Sol-Gel Routes.

The use of heterogeneous catalysts to increase the development of green chemistry is a rapidly growing area of research to save industry money. In this paper, mesoporous SiO2-Al2O3 mixed oxide supports with various Si/Al ratios were prepared using two different sol-gel routes: hydrolytic sol-gel (HSG) and non-hydrolytic sol-gel (NHSG). The HSG route was investigated in both acidic and basic media, while the NHSG was explored in the presence of ethanol and diisopropyl ether as oxygen donors. The resulting SiO2-Al2O3 mixed oxide supports were characterized using EDX, N2 physisorption, powder XRD, 29Si, 27Al MAS-NMR and NH3-TPD. The mesoporous SiO2-Al2O3 supports prepared by NHSG seemed to be more regularly distributed and also more acidic. Consequently, a simple one-step NHSG (ether and alcohol routes) was selected to prepare mesoporous and acidic SiO2-Al2O3-NiO mixed oxide catalysts, which were then evaluated in ethylene oligomerization. The samples prepared by the NHSG ether route showed better activity than those prepared by the NHSG alcohol route in the oligomerization of ethylene at 150 °C.

Prediction of axial capacity of corrosion-affected RC columns strengthened with inclusive FRP.

The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.

The influence of nanosunflower ash and nanowalnut shell ash on sustainable lightweight self-compacting concrete characteristics.

The absence of biodegradability exhibited by plastics is a matter of significant concern among environmentalists and scientists on a global scale. Therefore, it is essential to figure out potential pathways for the use of recycled plastics. The prospective applications of its utilisation in concrete are noteworthy. The use of recycled plastic into concrete, either as a partial or complete substitution for natural aggregates, addresses the issue of its proper disposal besides contributing to the preservation of natural aggregate resources. Furthermore, the use of agricultural wastes has been regarded as a very promising waste-based substance in the industry of concrete manufacturing, with the aim of fostering the creation of an environmentally sustainable construction material. This paper illustrates the impact of nano sunflower ash (NSFA) and nano walnut shells ash (NWSA) on durability (compressive strength and density after exposure to 800 °C and sulphate attack), mechanical properties (flexural, splitting tensile and compressive strength) and fresh characteristics (slump flow diameter, T50, V-funnel flow time, L-box height ratio, segregation resistance and density) of lightweight self-compacting concrete (LWSCC). The waste walnut shells and local Iraqi sunflower were calcinated at 700 ± 50 °C for 2 h and milled for 3 h using ball milling for producing NSFA and NWSA. The ball milling succeeded in reducing the particle size lower than 75 nm for NSFA and NWSA. The preparation of seven LWSCC concrete mixes was carried out to obtain a control mix, three mixtures were created using 10%, 20% and 30% NWSA, and the other three mixtures included 10%, 20% and 30% NSFA. The normal weight coarse aggregates were substituted by the plastic waste lightweight coarse aggregate with a ratio of 75%. The fresh LWSCC passing capacity, segregation resistance, and filling capability were evaluated. The hardened characteristics of LWSCC were evaluated by determining the flexural and splitting tensile strength at 7, 14 and 28 days and the compressive strength was measured at 7, 14, 28 and 60 days. Dry density and compressive strength were measured after exposing mixes to a temperature of 800 °C for 3 h and immersed in 10% magnesium sulphate attack. The results demonstrated that the LWSCC mechanical characteristics were reduced when the percentages of NWSA and NSFA increased, except for 10% NWSA substitution ratio which had an increase in splitting tensile strength test and similar flexural strength test to the control mixture. A minor change in mechanical characteristics was observed within the results of LWSCC dry density and compressive strength incorporating various NSFA and NWSA` contents after exposing to temperature 800 °C and immersed in 10% magnesium sulphate attack. Furthermore, according to the findings, it is possible to use a combination of materials consisting of 10-20% NSFA and 10-20% NWSA to produce LWSCC.

Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing.

This study conducts an extensive comparative analysis of computational intelligence approaches aimed at predicting the compressive strength (CS) of concrete, utilizing two non-destructive testing (NDT) methods: the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) test. In the ensemble learning approach, the six most popular algorithms (Adaboost, CatBoost, gradient boosting tree (GBT), random forest (RF), stacking, and extreme gradient boosting (XGB)) have been used to develop the prediction models of CS of concrete based on NDT. The ML models have been developed using a total of 721 samples, of which 111 were cast in the laboratory, 134 were obtained from in-situ testing, and the other samples were gathered from the literature. Among the three categories of analytical models-RH models, UPV models, and combined RH and UPV models; seven, ten, and thirteen models have been used respectively. AdaBoost, CatBoost, GBT, RF, Stacking, and XGB models have been used to improve the accuracy and dependability of the analytical models. The RH-M5, UPV-M6, and C-M6 (combined UPV and RH model) models were found with highest performance level amongst all the analytical models. The MAPE value of XGB was observed to be 84.37%, 83.24%, 77.33%, 59.46%, and 81.08% lower than AdaBoost, CatBoost, GBT, RF, and stacking, respectively. The performance of XGB model has been found best than other soft computing techniques and existing traditional predictive models.

Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions.

This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998-2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na+, Mg2+, Ca2+, SO42-, Cl-, HCO3-, K+, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson's Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems.

Effect of Shielding Gas Arc Welding Process on Cavitation Resistance of Welded Joints of AlMg4.5Mn Alloy.

The effect of the shielding gas arc welding process on the cavitation resistance of the three-component aluminum alloy AlMg4.5Mn and its welded joints was investigated. Welding was performed using the GTAW and GMAW processes in a shielded atmosphere of pure argon. After the welding, metallographic tests were performed, and the hardness distribution in the welded joints was determined. The ultrasonic vibration method was used to evaluate the base metal's and weld metal's resistance to cavitation. The change in mass was monitored to determine the cavitation rates. The morphology of the surface damage of the base metal and weld metal due to cavitation was monitored using scanning electron microscopy to explain the effect of the shielding gas arc welding process on their resistance to cavitation.

Crack Size and Undermatching Effects on Fracture Behavior of a Welded Joint.

Crack size and undermatching effects on fracture behavior of undermatched welded joints are presented and analyzed. Experimental and numerical analysis of the fracture behavior of high-strength low-alloyed (HSLA) steel welded joints with so-called small and large crack in undermatched weld metal and the base metal was performed, as a part of more extensive research previously conducted. J integral was determined by direct measurement using special instrumentation including strain gauges and a CMOD measuring device. Numerical analysis was performed by 3D finite element method (FEM) with different tensile properties in BM and WM. Results of J-CMOD curves evaluation for SUMITEN SM 80P HSLA steel and its weld metal (WM) are presented and analyzed for small and large cracks in tensile panels. This paper is focused on some new numerical results and observations on crack tip fields and constraint effects of undermatching and crack size keeping in mind previously performed experiments on the full-scale prototype. In this way, a unique combined approach of experimental investigation on the full-scale proto-type and tensile panels, as well as numerical investigation on mismatching and crack size effects, is achieved.

Modeling the Thermoelastic Bending of Ferric Oxide (Fe2O3) Nanoparticles-Enhanced RC Slabs.

Nanoparticles, by virtue of their amorphous nature and high specific surface area, exhibit ideal pozzolanic activity which leads to the formation of additional C-S-H gel by reacting with calcium hydroxide, resulting in a denser matrix. The proportions of ferric oxide (Fe2O3), silicon dioxide (SiO2), and aluminum oxide (Al2O3) in the clay, which interact chemically with the calcium oxide (CaO) during the clinkering reactions, influence the final properties of the cement and, therefore, of the concrete. Through the phases of this article, a refined trigonometric shear deformation theory (RTSDT), taking into account transverse shear deformation effects, is presented for the thermoelastic bending analysis of concrete slabs reinforced with ferric oxide (Fe2O3) nanoparticles. Thermoelastic properties are generated using Eshelby's model in order to determine the equivalent Young's modulus and thermal expansion of the nano-reinforced concrete slab. For an extended use of this study, the concrete plate is subjected to various mechanical and thermal loads. The governing equations of equilibrium are obtained using the principle of virtual work and solved using Navier's technique for simply supported plates. Numerical results are presented considering the effect of different variations such as volume percent of Fe2O3 nanoparticles, mechanical loads, thermal loads, and geometrical parameters on the thermoelastic bending of the plate. According to the results, the transverse displacement of concrete slabs subjected to mechanical loading and containing 30% nano-Fe2O3 was almost 45% lower than that of a slab without reinforcement, while the transverse displacement under thermal loadings increased by 10%.

A Brief Overview on Crack Patterns, Repair and Strengthening of Historical Masonry Structures.

Given that a significant fraction of buildings and architectural heritage in Europe's historical centers are masonry structures, the selection of proper diagnosis, technological surveys, non-destructive testing, and interpretations of crack and decay patterns is paramount for a risk assessment of possible damage. Identifying the possible crack patterns, discontinuities, and associated brittle failure mechanisms within unreinforced masonry under seismic and gravity actions allows for reliable retrofitting interventions. Traditional and modern materials and strengthening techniques create a wide range of compatible, removable, and sustainable conservation strategies. Steel/timber tie-rods are mainly used to support the horizontal thrust of arches, vaults, and roofs and are particularly suitable for better connecting structural elements, e.g., masonry walls and floors. Composite reinforcing systems using carbon, glass fibers, and thin mortar layers can improve tensile resistance, ultimate strength, and displacement capacity to avoid brittle shear failures. This study overviews masonry structural diagnostics and compares traditional and advanced strengthening techniques of masonry walls, arches, vaults, and columns. Several research results in automatic surface crack detection for unreinforced masonry (URM) walls are presented considering crack detection based on machine learning and deep learning algorithms. In addition, the kinematic and static principles of Limit Analysis within the rigid no-tension model framework are presented. The manuscript sets a practical perspective, providing an inclusive list of papers describing the essential latest research in this field; thus, this paper is useful for researchers and practitioners in masonry structures.

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams.

The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

Numerical and Experimental Investigations of Fracture Behaviour of Welded Joints with Multiple Defects.

Current standards related to welded joint defects (EN ISO 5817) only consider individual cases (i.e., single defect in a welded joint). The question remains about the behaviour of a welded joint in the simultaneous presence of several different types of defects, so-called multiple defects, which is the topic of this research. The main focus is on defects most commonly encountered in practice, such as linear misalignments, undercuts, incomplete root penetration, and excess weld metal. The welding procedure used in this case was metal active gas welding, a common technique when it comes to welding low-alloy low-carbon steels, including those used for pressure equipment. Different combinations of these defects were deliberately made in welded plates and tested in a standard way on a tensile machine, along with numerical simulations using the finite element method (FEM), based on real geometries. The goal was to predict the behaviour in terms of stress concentrations caused by geometry and affected by multiple defects and material heterogeneity. Numerical and experimental results were in good agreement, but only after some modifications of numerical models. The obtained stress values in the models ranged from noticeably lower than the yield stress of the used materials to slightly higher than it, suggesting that some defect combinations resulted in plastic strain, whereas other models remained in the elastic area. The stress-strain diagram obtained for the first group (misalignment, undercut, and excess root penetration) shows significantly less plasticity. Its yield stress is very close to its ultimate tensile strength, which in turn is noticeably lower compared with the other three groups. This suggests that welded joints with misalignment and incomplete root penetration are indeed the weakest of the four groups either due to the combination of the present defects or perhaps because of an additional unseen internal defect. From the other three diagrams, it can be concluded that the test specimens show very similar behaviour with nearly identical ultimate tensile strengths and considerable plasticity. The diagrams shows the most prominent yielding, with an easily distinguishable difference between the elastic and plastic regions. The diagrams are the most similar, having the same strain of around 9% and with a less obvious yield stress limit.

Nitrogen Functionalization of CVD Grown Three-Dimensional Graphene Foam for Hydrogen Evolution Reactions in Alkaline Media.

Three-dimensional graphene foam (3D-GrFoam) is a highly porous structure and sustained lattice formed by graphene layers with sp2 and sp3 hybridized carbon. In this work, chemical vapor deposition (CVD)-grown 3D-GrFoam was nitrogen-doped and platinum functionalized using hydrothermal treatment with different reducing agents (i.e., urea, hydrazine, ammonia, and dihydrogen hexachloroplatinate (IV) hydrate, respectively). X-ray photoelectron spectroscopy (XPS) survey showed that the most electrochemically active nitrogen-doped sample (GrFoam3N) contained 1.8 at % of N, and it exhibited a 172 mV dec-1 Tafel plot associated with the Volmer-Heyrovsky hydrogen evolution (HER) mechanism in 0.1 M KOH. By the hydrothermal process, 0.2 at % of platinum was anchored to the graphene foam surface, and the resultant sample of GrFoamPt yielded a value of 80 mV dec-1 Tafel associated with the Volmer-Tafel HER mechanism. Furthermore, Raman and infrared spectroscopy analysis, as well as scanning electron microscopy (SEM) were carried out to understand the structure of the samples.

Revelation on the Complex Nature of Mesoporous Hierarchical FAU-Y Zeolites.

The texture of mesoporous FAU-Y (FAUmes) prepared by surfactant-templating in basic media is a subject of debate. It is proposed that mesoporous FAU-Y consists of: (1) ordered mesoporous zeolite networks formed by a surfactant-assisted zeolite rearrangement process involving local dissolution and reconstruction of the crystalline framework, and (2) ordered mesoporous amorphous phases as Al-MCM-41, which coexist with zeolite nanodomains obtained by a dissolution-reassembly process. By the present systematic study, performed with FAU-Y (Si/Al = 15) in the presence of octadecyltrimethylammonium bromide and 0 < NaOH/Si ratio < 0.25 at 115 °C for 20 h, we demonstrate that mesoporous FAU zeolites consist, in fact, of a complex family of materials with textural features strongly impacted by the experimental conditions. Two main families have been disclosed: (1) for 0.0625 < NaOH/Si < 0.10, FAUmes are ordered mesoporous materials with zeolite walls, which coexist with zeolite nanodomains (100-200 nm) and (2) for 0.125 < NaOH/Si < 0.25, FAUmes are ordered mesoporous materials with amorphous walls as Al-MCM-41, which coexist with zeolite nanodomains (5-100 nm). The zeolite nanodomains decrease in size with the increase of NaOH/Si ratio. Increasing NaOH/Si ratio leads to an increase of mesopore volume, while the total surface area remains constant, and to a decrease of strong acidity in line with the decrease of micropore volume. The ordered mesoporous materials with zeolite walls feature the highest acidity strength. The ordered mesoporous materials with amorphous walls present additional large pores (50-200 nm), which increase in size and amount with the increase of NaOH/Si ratio. This alkaline treatment of FAU-Y represents a way to obtain ordered mesoporous materials with zeolite walls with high mesopore volume for NaOH/Si = 0.10 and a new way to synthesize mesoporous Al-MCM-41 materials containing extralarge pores (50-200 nm) ideal for optimal diffusion (NaOH/Si = 0.25).