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The purpose of this paper is to assess the effect of rubber on the durability and corrosion resistance of steel‐reinforced concrete by measuring the permeability, water absorption, corrosion potential, linear polarization resistance and AC impedance during 150 days immersion in NaCl solution.

Permeability, water absorption, corrosion potential, linear polarization resistance and AC impedance during 150 days immersion in NaCl solution have been measured. It was observed that the water permeability of rubberized concrete decreased with the addition of rubber. Scanning electron microscopy micrographs were used to study the bulk of rubberized concrete and it was shown that the interfacial zones of cement/rubber had an important effect on water permeability. The corrosion behavior of this concrete was investigated in the chloride solution.

It was observed that optimum corrosion resistance was obtained with low rubber contents.

The research results can be used for the fabrication of concrete modified with waste tire material.

The paper describes a method suitable for fabrication of concrete modified with tire waste, especially from an economic standpoint.

Evaluate the performance of progressive collapse of full-scale three-dimensional structure (3D) beam-slab substructures with and without the presence of reinforced concrete (RC) balconies using two concrete mixes [normal concrete (NC) and rubberized concrete (RuC)].

This study examines two concrete mixes to evaluate the progressive collapse performance of full-scale 3D beam-slab substructures with and without the presence of RC balconies using the finite element (FE) method.

The results showed that the vertical loads that affect the structures of the specimens after including the balconies in the modeling increased by an average of 29.3% compared with those of the specimens without balconies. The specimens with balconies exhibited higher resistance to progressive collapse in comparison with the specimens without balconies. Moreover, the RuC specimens performed very efficiently during the catenary stage, which significantly enhanced robustness to substantial deformation to delay or mitigate the progressive collapse risk.

All the experimental and numerical studies of the RC beam-slab substructures under progressive collapse scenarios are limited and do not consider the balcony’s presence in the building. Although balconies represent a common feature of multistory residential buildings, their presence in the building has more likely caused the failure of this building compared with a building without balconies. However, balconies are an external extension of RC slabs, which can provide extra resistance through tensile membrane action (TMA) or compressive membrane action (CMA). All those gaps have not been investigated yet.

Currently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.

In this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.

In general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.

Previous studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.

Roller compacted concrete (RCC) pavement is used in areas subjected to heavy impact loads; therefore, higher impact resistance is a desirable property of consideration. This study aims to investigate the effect of partial replacement of fine aggregate with crumb rubber (CR) and the addition of nanosilica (NS) by weight of cementitious materials on the impact resistance of roller compacted rubbercrete (RCR).

Four replacement levels of CR (0, 10, 20 and 30 per cent) and four addition levels of NS (0, 1, 2 and 3 per cent) were considered. The impact resistance test was carried out using the drop weight test recommended by ACI 544.

The results showed that the impact resistance of RCR increases with an increase in both CR and NS addition, though for CR above 20 per cent, sudden drop in impact resistance was observed. However, NS reduces the ductility of RCR by decreasing the post-cracking impact resistance. Response surface methodology was used to develop models for predicting the impact resistance of RCR, and the developed models showed a high degree of correlation. As a result of wide variations in the impact drop test data, two-parameter Weibull distribution function was used for the data analysis, and it was found that the probabilistic distributions of the first crack and ultimate failure impact resistance follow the two-parameter Weibull distribution function.

In this work, the effect of partial replacement of fine aggregate with CR and the addition of NS by weight of cementitious materials on the impact resistance of RCC pavement has been investigated. CR has been used to increase the impact resistance of RCC Pavement.

The purpose of this paper is to focus on compressive strength modelling of cementitious mixtures like mortar and Roller-compacted concrete (RCC) containing rubber aggregates from shredded worn tires and filler using adaptive neuro fuzzy inference systems (ANFIS).

The volume substitution contains a ratio of rubber aggregates vs sand in mortar and with crushed sand in RCC and ranges from 0 to 50 per cent. As for the filler, they are substituted with sand by 5 per cent in mortar mixture. The methodology consists of optimizing the percentage of substitution in cementitious mixtures to ensure better mechanical properties of materials like compressive strength. The prediction of compressive strength and the optimization of cementitious mixtures encourage their uses in such construction pavements, in area games or in other special constructions. These cementitious materials are considered as friendly to the environment by focussing on their improved deformability.

The results of this paper show that the performance of the constructed fuzzy method was measured by correlation of experimental and model results of mortar and RCC mixtures containing both rubber aggregates and filler. The comparison between elaborated models through the error and the accuracy calculations confirms the reliability of the ANFIS method.

The purpose of this paper is to assess the performance of the constructed fuzzy model by the ANFIS method for two types of cementitious materials like mortar and RCC containing rubber aggregates and filler. The fuzzy method could predict the compressive strength based on the limited measurement values in the mechanical experiment. Furthermore, the comparison between the elaborated models confirms the reliability of the ANFIS method through the error and the accuracy calculations for the best cementitious material mixtures.

The initiative for sustainability in the construction industry has led to the innovative utilization of automobile tire waste, transforming it into value-added products, toward decarbonization in the construction industry, aligning with the development and sustainability goals of Al-Kharj Governorate. However, the disposal of these materials generates significant environmental concerns. As a payoff for these efforts, this study aims to contribute to a fruitful shift toward eco-friendly recycling techniques, particularly by studying the transformation of tire waste bead wires into recycled steel tire fibers (RSTFs) for sustainable concrete composites.

This research delves into how this technological transformation not only addresses environmental concerns but also propels sustainable tire innovation forward, presenting a promising solution for waste management and material efficiency in building materials. Recent studies have highlighted the superior tensile strength of RSTFs from discarded tires, making them suitable for various structural engineering applications. Recently, there has been a notable shift in research focus to the use of RSTFs as an alternative to traditional fibers in concrete. In this study, however, efforts have paid off in outlining a comprehensive assessment to investigate the viability and efficacy of repurposing tire bead wires into RSTFs for use in concrete composites, as reported in the literature.

This study examined the Saudi waste management, the geometrical properties of RSTFs, and their impact on the strength properties of concrete microstructure. It also examined the economic, cost, and environmental impacts of RSTFs on concrete composites, underscoring the need for the construction industry to adopt more sustainable and adaptable practices. Furthermore, the main findings of this study are proposed insights and a blueprint for the construction industry in Al-Kharj Governorate, calling for collective action from both public and private sectors, and the community to transform challenges into job opportunities for growth and sustainability.

This study pointed to thoroughly demonstrate the technological advancement in converting tire waste to reinforcing fibers by evaluating the effectiveness, environmental sustainability, and practicality of these fibers in eco-friendly concrete composites. Besides, the desired properties and standards for RSTFs to enhance the structural integrity of concrete composites are recommended, as is the need to establish protocols and further study into the long-term efficacy of RSTF-reinforced concrete composites.

Macro synthetic steel fibers were incorporated into the concrete material as a toughening agent to improve the corrosion and cracking resistances of concrete in a sulfate-containing service environment.

To study the basic mechanical properties of this system, an accelerated concrete degradation test was designed to evaluate the influence of the sulfate ions on the concrete. A three-point bending test was carried out in the laboratory to evaluate the fracture toughness. The thickness of the damaged concrete layer and changes of microstructure of the degraded concrete were monitored by using ultrasound, scanning electron microscopy and X-ray diffraction detection methods.

The results showed that compared to the performance of ordinary concrete, in an exposure environment containing sulfate ions, the structure compactness of macro synthetic steel fiber concrete was improved, degradation resistance to the sulfate solution was enhanced and the fracture resistance performance was improved significantly.

The thickness of the degradation layer on the macro synthetic steel fiber concrete was less than a half of that of ordinary concrete in the sulfate environment, and was generally unchanged with increase in the sulfate concentration. Through micro-structural analysis, it was confirmed that macro synthetic steel fiber improved the compactness of the concrete structure, inhibiting access of sulfate ions to the interior of the concrete and thereby reducing the degree of sulfate degradation to the concrete.

This paper presents the effects of replacing fine aggregate (FA) with waste foundry sand (WFS) in natural aggregate and construction waste aggregate concrete specimens without and with superplasticizer (SP), silica fume (SF) and fiber (F) to solve the disposal problems of various wastes along with saving the environment. This study aims to investigate the effect of construction waste, WFS along with additives on the stress-strain behavior and development of compressive strength with age.

The various concrete specimen were prepared in mix proportion of 1: 2: 4 (cement (C): sand: coarse aggregate). The water-cement ratio of 0.5 (decreased by 10% for samples containing SP) to grading 1: 2: 4 under air-dry condition was adopted in the preparation of concrete specimens. The compressive strength of various concrete specimen were noticed for 3, 7 and 28 days by applying load through universal testing machine.

Upon adding construction and demolition waste aggregates, the compressive strength of concrete after 28 days was comparable to that of the control concrete specimen. An enhancement in the value of compressive strength is perceived when FA is replaced with WFS to the extent of 10%, 20% and 30%. If both construction and demolition waste aggregate and WFS replacing FA are used, the compressive strength increases. When FA is interchanged with WFS in natural aggregate or construction demolition waste aggregate concrete including usage of SF or F, the compressive strength improves significantly. Further, when construction and demolition waste aggregate and WFS replacing FA including SP are used, the compressive strength improves marginally compared to that of control specimen. The rate of strength development with age is observed to follow similar trend as in control concrete specimen. Therefore, construction and demolition waste and or WFS can be used effectively in concrete confirming an improvement in strength.

The utilization of these wastes in concrete will resolve the problem of their disposal and save the environment.

This paper aims to clarify the efficient process of the machine learning algorithms implemented in the ready-mix concrete (RMC) onsite. It proposes innovative machine learning algorithms in terms of preciseness and computation time for the RMC strength prediction.

This paper presents an investigation of five different machine learning algorithms, namely, multilinear regression, support vector regression, k-nearest neighbors, extreme gradient boosting (XGBOOST) and deep neural network (DNN), that can be used to predict the 28- and 56-day compressive strengths of nine mix designs and four mixing conditions. Two algorithms were designated for fitting the actual and predicted 28- and 56-day compressive strength data. Moreover, the 28-day compressive strength data were implemented to predict 56-day compressive strength.

The efficacy of the compressive strength data was predicted by DNN and XGBOOST algorithms. The computation time of the XGBOOST algorithm was apparently faster than the DNN, offering it to be the most suitable strength prediction tool for RMC.

Since none has been practically adopted the machine learning for strength prediction for RMC, the scope of this work focuses on the commercially available algorithms. The adoption of the modified methods to fit with the RMC data should be determined thereafter.

The selected algorithms offer efficient prediction for promoting sustainability to the RMC industries. The standard adopting such algorithms can be established, excluding the traditional labor testing. The manufacturers can implement research to introduce machine learning in the quality controcl process of their plants.

Regarding literature review, machine learning has been assessed regarding the laboratory concrete mix design and concrete performance. A study conducted based on the on-site production and prolonged mixing parameters is lacking.

Soil is an essential component of road construction and is used in the form of subgrade materials. It ensures the stability and durability of the road under adverse conditions; being one of the important parameters, poor judgment of the engineering properties of soil can lead to pavement failure. Geopathic stress (GS) is a subtle energy in the form of harmful electromagnetic radiation. This study aims to investigate the effect of GS on soil and concrete.

A total of 23 soil samples from stress zones and nonstress zones were tested for different engineering properties like water content, liquid limit, plastic limit, specific gravity and California bearing ratio. Two concrete panels were placed on GS zones, and their quality was monitored through nondestructive testing for a period of one year.

The result shows that the engineering properties of soil and pavement thickness are increasing in stress zones as compared with nonstress zones. For concrete panels, as time passes, the quality of the concrete gets reduced, which hints toward the detrimental effect of GS.

This research is a systematic, scientific, reliable study which evaluated subgrade characteristics thus determining the detrimental impact of the GS on soil and pavement thickness. On a concluding note, this study provides a detailed insight into the performance of the road segment when subjected to GS. Through this investigation, it is recommended that GS should be considered in the design of roads.