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The prediction of the ultimate bending capacity of the rectangular concrete-filled steel tube (RCFST) beams strengthened with U-shaped carbon fiber reinforced polymer (CFRP) sheets is limited to using the existing empirical models. Thus, this study aims to develop a new theoretical model based on a stress block model to predict the ultimate bending capacity (Mu) of the RCFST beams strengthened with a U-shaped CFRP-wrapping scheme. For this purpose, 28 finite element (FE) models of CFRP-strengthened RCFST beams had been analyzed for further investigation of the flexural behavior and longitudinal stresses distributed along with the beam’s components (steel tube, concrete core, and CFRP layers). The main parameters investigated are concrete compressive strength, steel yield strength, number of CFRP layers, and CFRP-wrapping-depth ratio. In addition, the Mu values obtained from the FE models of the current study and those from the existing experimental tests performed by others are used to verify the corresponding values that are theoretically predicted by the new model. The comparison showed that the proposed model is moderately conservative, as the predicted values of Mu are, on average, up to 10% lower than those obtained from experimental tests and FE analysis.

High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.

Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80% of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the particle swarm optimization (PSO) technique. It was discovered in this investigation that the new stress-strain model predictions are consistent with the remaining 20% of the experimental stress-strain curves obtained. Case studies of hybrid–fiber–reinforced concrete constructions were investigated in order to better understand the behavior of such elements. The data revealed that the proposed model has the highest absolute relative error (ARE) frequencies (ARE 10%) and the lowest absolute relative error (ARE > 15%) frequencies (ARE > 15%).

Salient features of lightweight aggregate concrete (LAC) include noticeable fire resistance, high strength-to-weight ratio, and low magnitude of dead loads. Further, LAC has a low cost, eases construction practices, and possesses an environment-friendly nature. On the downside, LAC has substandard mechanical properties in comparison to normal aggregate concrete. Natural fiber-reinforced polymers (FRPs) have shown their potential in ameliorating the mechanical properties of natural aggregate concrete. So far, no study has been conducted to assess the efficacy of hemp rope confinement to strengthen lightweight aggregate concrete especially comprising rectilinear sections. This study aimed to overcome the substandard nature of LAC. A low-cost, sustainable, and environmentally green solution in the form of natural hemp rope layers is proposed. Twenty-four square concrete specimens were tested in three groups depending upon the presence and quantity of lightweight aggregates. It was found that concrete constructed with lightweight aggregates demonstrated lower ultimate compressive strength and strain as compared to normal aggregate concrete. Hemp rope-confined LAC showed enhanced ultimate compressive strength and strain. This enhancement was found to increase with the number of hemp rope layers. Several existing ultimate stress models were assessed to predict the ultimate compressive strength of the hemp rope-confined specimens. Only a single model was able to predict the ultimate compressive strength of the hemp rope-confined specimens with reasonable accuracy.

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector (PRSC) is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression (SVR), artificial neural network (ANN), and M5 tree model. In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model.

To enhance the structural performance of concrete-filled steel tube (CFST) columns, various strengthening techniques have been proposed, including the use of internal steel stiffeners, external wrapping with carbon fiber-reinforced polymer (CFRP) sheets, and embedded steel elements. However, the behavior of concrete-filled stainless-steel tube (CFSST) columns remains insufficiently explored. This study numerically investigates the axial performance of square CFSST columns internally strengthened with embedded I-section steel profiles under biaxial eccentric loading. Finite element (FE) simulations were conducted using ABAQUS v. 6.2, and the developed models were validated against experimental results from the literature. A comprehensive parametric study was performed to evaluate the effects of several variables, including concrete compressive strength (fcu), stainless-steel yield strength (fy), the depth ratio between the stainless-steel tube and the internal I-section (Dst/Dsi), biaxial eccentricities (ex and ey), and tube thickness (t). The results demonstrated that the axial performance of CFSST columns was most significantly influenced by increasing the Dst/Dsi ratio and load eccentricities. In contrast, increasing the concrete strength and steel yield strength had relatively modest effects. Specifically, the ultimate axial capacity increased by 9.97% when the steel yield strength rose from 550 MPa to 650 MPa and by 33.72% when the tube thickness increased from 3.0 mm to 5.0 mm. A strength gain of only 10.23% was observed when the concrete strength increased from 30 MPa to 60 MPa. Moreover, the energy absorption index of the strengthened columns improved in correlation with the enhanced axial capacities.

Composite slim floor systems are increasingly applied in the construction industry. Therefore, several experimental and numerical studies have been conducted to investigate the composite flooring systems and examine the various details that may affect the production of composite floors. This review paper summarises some important developments of composite slim floor beams, such as the type of steel beam cross section and shear connectors used, presenting essential results and important notes from various studies. The fundamental structural elements and the contribution of conducted studies towards improving the shear capacity of composite beams in enhancing the general structural behaviour were also described. Finally, the paper concludes with the potential of a deeper investigation of some issues that accompany the application of certain types of shear connects or steel shapes used to improve the composite slim floor system so that the improvement construction industry may make the most out of using composite construction techniques.

The naturally fractured reservoirs are one of the most challenging due to the tectonic movements that are caused to increase the permeability and conductivity of the fractures. The instability of the permeability and conductivity effects on the fluid flow path causes problems during the transfer of the fluids from the matrix to the fractures and fluid losses during production. In addition, these complications made it difficult for engineers to estimate fluid flow during production. The fracture properties’ study is important to model the fluid flow paths such as the fracture porosity, permeability, and the shape factor, which are considered essential in the stability of fluid flow. To examine this, this research introduced new models including decision tree (DT), random forest (RF), K-nearest regression (KNR), ridge regression (RR), and LASSO regression model,. The research studied the fracture properties in naturally fractured reservoirs like the fracture porosity (FP) and the shape factor (SF). The datasets used in this study were collected from previous studies “i.e., Texas oil and gas fields” to build an intelligence-based predictive model for fluid flow characteristics. The prediction process was conducted based on interporosity flow coefficient, storativity ratio, wellbore radius, matrix permeability, and fracture permeability as input data. This study revealed a positive finding for the adopted machine learning (ML) models and was superior in using statistical accuracy metrics. Overall, the research emphasized the implementation of computer-aided models for naturally fractured reservoir analysis, giving more details on the extensive execution techniques, such as injection or the creation of artificial cracks, to minimize hydrocarbon losses or leakage.

For decades, ferrocement has been used to repair, strengthen, and even build structural components because it is a long-lasting and reasonably priced material. However, onsite ferrocement jacketing is time-consuming and labour-intensive. Alternatively, prefabricated ferrocement jacket installation eliminates these shortcomings. Therefore, this study utilises wearable prefabricated ferrocement jackets to repair and strengthen axially loaded sub-standard low-strength concrete elements. In order to repair cracked specimens and strengthen existing intact specimens, two types of wearable prefabricated jackets are proposed, ‘L’ shape and ‘U’ shape. Besides a control specimen, two preloaded and two unloaded square concrete specimens were utilised to repair and strengthen using the Prefabricated Ferrocement Jacketing system. The test results and crack patterns show that all the jacketed specimens performed better than the control specimens in terms of load-bearing capacity, ultimate axial and lateral deflection, and ductility. In terms of load-bearing capacity, the unloaded strengthened specimens showed significant results consistently. Based on the results, the proposed solutions were found to be effective in solving the problem of typical square ferrocement jackets.

In this study, the flexural performance of a new composite beam–slab system filled with concrete material was investigated, where this system was mainly prepared from lightweight cold-formed steel sections of a beam and a deck slab for carrying heavy floor loads as another concept of a conventional composite system with a lower cost impact. For this purpose, seven samples of a profile steel sheet–dry board deck slab (PSSDB/PDS) carried by a steel cold-formed C-purlins beam (CB) were prepared and named “composite CBPDS specimen”, which were tested under a static bending load. Specifically, the effects of the profile steel sheet (PSS) direction (parallel or perpendicular to the span of the specimen) using different C-purlins configurations (double sections connected face-to-face, double separate sections, and a single section) were investigated. The research discussed the specimens’ failure modes, flexural behavior, bending capacity, bending strain relationships, and energy absorption index of specimens. Generally, the CBPDS specimens with the PSS slab placed in a parallel direction achieved approximately a 13–40% higher bending capacity compared with the corresponding specimens with a perpendicular PSS direction (depending on the configuration of the beam). Fabricating the beam of the CBPDS specimen with double C-purlins (face-to-face) led to more effective concrete confinement behavior compared with the double separate C-purlins beam. The related specimen recorded a 10% higher bending capacity. Finally, the suggested composite CBPDS system exhibited a sufficient energy absorption capability of the static bending load because it demonstrated high strength and high ductility.