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Present study focuses on scope of developing sustainable heat resistant concrete by adding steel fibre (Sf) and polypropylene fibre (PPf) along with partially replacement of ordinary portland cement (OPC) and natural fine aggregate with fly ash (FA) and granular blast furnace slag (GBFS). Replacement percentages of FA and GBFS were 40% and 50%, whereas Sf and PPf for fibre-added mixes were 1% by volume of concrete and 0.25% by weight of cement, respectively.

An experimental work had been carried out to make comparison between control mix (CM), fibre-added sustainable mix (SCMF) and fibre-added control mix (CMF) with reference to weight loss, mechanical strength (compressive, split and flexure) after exposed to room temperature (27°C) to 1000°C at the interval of 200°C for 4 h of heat curing followed by furnace cooling and then natural cooling. Furthermore, microstructural analysis was executed at 27°C, 400°C and 800°C, respectively.

Colour change and hair line cracks were started to appear at 600°C. Fibre-added control mix and sustainable mix did not exhibit any significant cracks as compared to control mix even at 1000°C. Major losses were occurred at temperature higher than 600°C, loss in compressive strength was about 70% in control mix, while 60% in fibre-added mixes. SCMF exhibited the highest retention of strength with respect to all cases of mechanical strength.

Present study is based on the slow heating condition followed by longer duration of heat curing at target temperature.

Present work can be helpful for the design engineer for assessing the fire deterioration of concrete structure existing near the fire establishment such as furnace and ovens. Building fire (high temperature for short duration) might be the further scope of work.

Concept of incorporating pozzolanic binder and calcareous fine aggregate was adopted to take the advantage pozzolanacity and fire resistivity. To the best of author’s knowledge, there is a scope for fill the research gap in this area.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.

One of the often-employed building constituents in the construction sector is concrete, which involves hydration of cement, leading to the generation of carbon footprints during its production. Also, massive amount of natural aggregate is illegally mined, which poses serious environmental issues along with ecological misbalance. Researchers are in continuous search of appropriate substitutes to mitigate those challenges and develop innovative concrete mix. Consequently, depletion of natural resources, the disturbances to the environmental and ecological imbalance will reduce. The purpose of this study is to develop a Portland Slag Cement based novel sustainable concrete incorporating Alccofine and Recycled Refractory Brick as fractional replacement of cement and fine aggregate, respectively and evaluate its destructive, non-destructive and microstructural properties.

M25 grade of concrete adopting 0.45 water-binder proportion, with diverse percentage of Alccofine as fractional substitution of cement and 20% of recycled refractory brick (RRB) as fine aggregate, has been cast and evaluated for diverse mechanical strength following a curing of 7, 14 and 28 days. Scanning electron microscopic analysis has been carried out to study the microstructural changes in the specimens.

Supplementary use of Alccofine enhanced normal compressive strength of sustainable concrete mix blended with Portland Slag Cement by a large amount at all levels of 7, 14 and 28 days of curing. Test results indicated development of a favourable high-strength sustainable concrete mix by substituting cement with Alccofine.

This manuscript has demonstrated the possibility of developing sustainable concrete blends by incorporating Alccofine 1203 and RRB as partial replacement of Portland Slag Cement and natural fine aggregate, respectively. The strength and potential of concrete incorporating RRB for wider and special application in adverse environmental conditions having higher thermal gradient, as RRB is a valuable waste from high temperature kiln and furnaces. Alccofine 1203 has been included in the concrete mix as an alternative to Portland Slag Cement to improve the mechanical strength properties and durability of concrete intended for adverse environmental application.

Catastrophe of steel-structured bridges due to progressive localized corrosion may lead to a major loss in terms of life and cost if not monitored continuously or periodically. The purpose of this paper is to present a vibration-based strategy to assess the severity and monitor the deterioration caused by corrosion-induced localized damage in a simply-supported steel beam.

The threshold damage level is defined up to the yield limit of a simply supported steel beam of size ISMB 150 × 8 × 5 under three-point bending test and the progressive damage is induced through a continuous accelerated corrosion test. Change in the fundamental natural frequency due to localized damage in the experimental beam and the modulus of elasticity (E) in the corroded zone of an updated finite element (FE) model is evaluated.

The updated FE model of the damaged beam shows a clear trend with the progressive damage of the beam and, hence, can be used to monitor the severity of damage and remaining capacity assessment of the monitored beam.

Steel-structured bridges are prone to localized corrosion attack, and there are no standardized process or predictive model available by international steel design codes on how to consider corrosion damage in the condition assessment analysis. The vibration-based methods have gained popularity for condition assessment, and are mostly confined to damage assessment of corroded reinforced concrete (RC) beams. In this work, a vibration-based approach is presented for degradation assessment of steel beam due to progressive localized corrosion using modal hammer test.