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This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.

The combination of an Engineered Cementitious Composite (ECC) layer and steel plate to reinforce RC beams (ESRB) is a new strengthening method. The ESRB was proposed based on the steel plate at the bottom of RC beams, aiming to solve the problem of over-reinforced RC beams and improve the bearing capacity of RC beams without affecting their ductility.

In this paper, the finite element model of ESRB was established by ABAQUS. The results were compared with the experimental results of ESRB in previous studies and the reliability of the finite element model was verified. On this basis, parameters such as the width of the steel plate, thickness of the ECC layer, damage degree of the original beam and cross-sectional area of longitudinal tensile rebar were analyzed by the verified finite element model. Based on the load–deflection curve of ESRB, ESRB was discussed in terms of ultimate bearing capacity and ductility.

The results demonstrate that when the width of the steel plate increases, the ultimate load of ESRB increases to 133.22 kN by 11.58% as well as the ductility index increases to 2.39. With the increase of the damage degree of the original beam, the ultimate load of ESRB decreases by 23.7%–91.09 kN and the ductility index decreases to 1.90. With the enhancement of the cross-sectional area of longitudinal tensile rebar, the ultimate bearing capacity of ESRB increases to 126.75 kN by 6.2% and the ductility index elevates to 2.30. Finally, a calculation model for predicting the flexural capacity of ESRB is proposed. The calculated results of the model are in line with the experimental results.

Based on the comparative analysis of the test results and numerical simulation results of 11 test beams, this investigation verified the accuracy and reliability of the finite element simulation from the aspects of load–deflection curve, characteristic load and failure mode. Furthermore, based on load–deflection curve, the effects of steel plate width, ECC layer thickness, damage degree of the original beam and cross-sectional area of longitudinal tensile rebar on the ultimate bearing capacity and ductility of ESRB were discussed. Finally, a simplified method was put forward to further verify the effectiveness of ESRB through analytical calculation.

The aim of this paper is to determine the thermal conductivity of a protective layer of alkali-activated cement and the possibility of performing fire protection with fireclay sand and Lightweight mortar. Unprotected steel structures have generally low fire resistance and require surface protection. The design of passive protection of a steel element must consider the service life of the structure and the possible need to replace the fire protection layer. Currently, conventional passive protection options include intumescent coatings, which are subject to frequent inspection and renewal, gypsum and cement-based fire coatings and gypsum and cement board fire protection.

Alkali-activated cements provide an alternative to traditional Portland clinker-based materials for specific areas. This paper presents the properties of hybrid cement, its manufacturability for conventional mortars and the development of passive fire protection. Fire experiments were conducted with mortar with alkali-activated and fireclay sand and lightweight mortar with alkali-activated cement and expanded perlite. Fire experiment FE modelling.

The temperatures of the protected steel and the formation of cracks in the protective layer were investigated. Based on the experiments, the thermal conductivities of the two protective layers were determined. Conclusions are presented on the applicability of alkaline-activated cement mortars and the possibilities of applicability for the protection of steel structures. The functionality of the passive fire layer was confirmed and the strengths of the mortar used were determined. The use of alkali-activated cements was shown to be a suitable option for sustainable passive fire protection of steel structures.

Eco-friendly fire protection based on hybrid alkali-activated cement of steel members.

The purpose of this paper is to evaluate the effect of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃) nanoparticles-reinforced pure aluminium (Al) on the mechanical properties of hybrid aluminium matrix nanocomposites (HAMNCs).

The HAMNCs were fabricated via a vacuum die-assisted stir casting route by a two-step feeding method. The varying weight percentages of TiO₂ and Y₂O₃ nanoparticles were added as 2.5, 5, 7.5 and 10 Wt.%.

Scanning electron microscope images showed the homogenous dispersion of nanoparticles in Al matrix. The tensile strength by 28.97%, yield strength by 50.60%, compression strength by 104.6% and micro-hardness by 50.90% were improved in HAMNC1 when compared to the base matrix. The highest values impact strength of 36.3 J was observed for HAMNC1. The elongation % was decreased by increasing the weight percentage of the nanoparticles. HAMNC1 improved the wear resistance by 23.68%, while increasing the coefficient of friction by 14.18%. Field emission scanning electron microscope analysis of the fractured surfaces of tensile samples revealed microcracks and the debonding of nanoparticles.

The combined effect of TiO₂ and Y₂O₃ nanoparticles with pure Al on mechanical properties has been studied. The composites were fabricated with two-step feeding vacuum-assisted stir casting.

The long-span continuous rigid-frame bridges are commonly constructed by the section-by-section symmetrical balance suspension casting method. The deflection of these bridges is increasing over time. Wet joints are a typical construction feature of continuous rigid-frame bridges and will affect their integrity. To investigate the sensitivity of shear surface quality on the mechanical properties of long-span prestressed continuous rigid-frame bridges, a large serviced bridge is selected for analysis.

Its shear surface is examined and classified using the damage measuring method, and four levels are determined statistically based on the core sample integrity, cracking length and cracking depth. Based on the shear-friction theory of the shear surface, a 3D solid element-based finite element model of the selected bridge is established, taking into account factors such as damage location, damage number and damage of the shear surface. The simulated results on the stress distribution of the local segment, the shear surface opening and the beam deflection are extracted and analyzed.

The findings indicate that the main factors affecting the ultimate shear stress and shear strength of the shear surface are size, shear reinforcements, normal stress and friction performance of the shear surface. The connection strength of a single or a few shear surfaces decreases but with little effect on the local stress. Cracking and opening mainly occur at the 1/4 span. Compared with the rigid “Tie” connection, the mid-span deflection of the main span increases by 25.03% and the relative deflection of the section near the shear surface increases by 99.89%. However, when there are penetrating cracks and openings in the shear surface at the 1/2 span, compared with the 1/4 span position, the mid-span deflection of the main span and the relative deflection of the cross-section increase by 4.50%. The deflection of the main span increases with the failure of the shear surface.

These conclusions can guide the analysis of deflection development in long-span prestressed continuous rigid-frame bridges.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

The current study mainly focuses on the effect of varying diameter recycled steel fibers (RSF) on mechanical properties of concrete prepared with 25 and 50% of recycled coarse aggregate (RCA) as well as 100% natural aggregate (NA). Two types of RSF with 0.84 mm and 1.24 mm diameter having 30 mm length were incorporated into normal and recycled aggregate concrete (RAC).

The fresh behavior, compressive, splitting tensile, flexural strengths and modulus of elasticity of all the mixes were investigated to evaluate the mechanical properties of RACs. In addition, specimen crack and testing co-relation were analyzed to evaluate fiber response in the RAC.

According to the experimental results, it was observed that mechanical properties decreased with the increment replacement of NA by RCA. However, the RSF greatly improves the mechanical properties of both normal concrete and RACs. Moreover, mixes containing 1.24 mm diameter RSF had a more significant positive impact on mechanical properties than mixes containing 0.84 mm diameter RSF. The 0.84 mm and 1.24 mm RSF addition improved the mixes' compressive, splitting tensile and flexural strength by 10%–19%, 19%–30% and 3%–11%, respectively when compared to the null fiber mix. Therefore, based on the mechanical properties, the 1.24 mm diameter of RSF with 25% replacement of RCA was obtained as an optimum solution in terms of performance improvement, environmental benefit and economic cost.

The practice of RCA in construction is a long-term strategy for reducing natural resource extraction and the negative ecological impact of waste concrete.

This is the first study on the effects of varying size (0.84 mm and 1.24 mm diameter) RSF on the mechanical properties of RAC. Additionally, varying sizes of RSF and silica fume added a new dimension to the RAC.

The use of waste polyethylene terephthalate (PET) plastics derived from shredded bottles in concrete is not formalized yet, especially in reinforced members such as beams and columns. The disposal of plastic wastes in concrete is a viable alternative to manage those wastes while minimizing the environmental impacts associated to recycling, carbon dioxide emissions and energy consumption.

This paper evaluates the suitability of 2D deterministic and stochastic finite element (FE) modeling to predict the shear strength behavior of reinforced concrete (RC) beams without stirrups. Different concrete mixtures prepared with 1.5%–4.5% PET additions, by volume, are investigated.

Test results showed that the deterministic and stochastic FE approaches are accurate to assess the maximum load of RC beams at failure and corresponding midspan deflection. However, the crack patterns observed experimentally during the different stages of loading can only be reproduced using the stochastic FE approach. This later method accounts for the concrete heterogeneity due to PET additions, allowing a statistical simulation of the effect of mechanical properties (i.e. compressive strength, tensile strength and Young’s modulus) on the output FE parameters.

Data presented in this paper can be of interest to civil and structural engineers, aiming to predict the failure mechanisms of RC beams containing plastic wastes, while minimizing the experimental time and resources needed to estimate the variability effect of concrete properties on the performance of such structures.

To make full use of the tensile strength of near surface mounting (NSM) pasted carbon fiber reinforced plastics (CFRP) strips and further increase the flexural bearing capacity and flexibility of reinforced concrete (RC) beams, a new composite reinforcement method using ultra-high performance concrete (UHPC) layer in the compression zone of RC beams is submitted based on embedding CFRP strips in the tension zone of RC beams. This paper aims to discuss the aforementioned points.

The experimental beam was simulated by ABAQUS, and compared with the experimental results, the validity of the finite element model was verified. On this basis, the reinforced RC beam is used as the control beam, and parameters such as the CFRP strip number, UHPC layer thickness, steel bar ratio and concrete strength are studied through the verified model. In addition, the numerical calculation results of yield strength, ultimate strength, failure deflection and flexibility are also given.

The flexural bearing capacity of RC beams supported by the new method is 132.3% higher than that of unreinforced beams, and 7.8% higher than that of RC beams supported only with CFRP strips. The deflection flexibility coefficient of the new reinforced RC beam is 8.06, which is higher than that of the unreinforced beam and the reinforced concrete beam with only CFRP strips embedded in the tension zone.

In this paper, a new reinforcement method is submitted, and the effects of various parameters on the ultimate bearing capacity and flexibility of reinforced RC beams are analyzed by the finite element numerical simulation. Finally, the effectiveness of the new method is verified by the analytical formula.

To contribute to the identification of the parameters influencing the behavior of textile-reinforced concrete (TRC), the purpose of this paper is to investigate the flexural behavior of TRC-based plates under four-point bending notably designed in the context of sustainable development and the substitution of mortar components with natural and abundant materials.

An extensive experimental campaign was focused about two main parameters. The first one emphases the textile reinforcements, such as the number of layers, the nature and the textile mesh size. In the second step, the composition of the mortar matrix was explored through the use of dune sand as a substitute of the river one.

Test results in terms of load-displacement response and failure patterns were highlighted, discussed and confronted to literature ones. As key findings, an increase of the load-bearing capacity and ductility, comparable to the use of an industrially produced second textile layer was recorded with the use of dune sand in the mortar mix design. The designed ecofriendly samples with economic concerns denote the significance of obtained outcomes in this research study.

The novelty of the present work was to valorize the use of natural dune sand to design new TRC samples to respond to the environmental and economical requirements. The obtained values provide an improved textiles–matrix interface performance compared to classical TRC samples issued from the literature.