Self-cured concrete is a type of cement-based material that has the unique ability to mitigate the loss rate of water and increase the capacity of concrete to retain water compared to conventional concrete. The technique allows a water-filled internal curing agent to be added to the concrete mixture and then slowly releases water during the hydration process. Many researchers have studied the composition of self-curing concrete using different materials such as artificial lightweight aggregate (LWA), porous superfine powders, superabsorbent polymers (SAP), polyethylene glycol (PEG), natural fibers, and artificial normal-weight aggregate (ANWA) as curing agents. Likewise, physical, mechanical, and microstructure properties, including the mechanisms of curing agents toward self-curing cement-based, were discussed. It was suggested that adopting self-curing agents in concrete has a beneficial effect on hydration, improving the mechanical properties, durability, cracking susceptibility behavior, and mitigating autogenous and drying shrinkage. The interfacial transition zone (ITZ) between the curing agent and the cement paste matrix also improved, and the permeability is reduced.

Alkali activated concretes have emerged as a prospective alternative to conventional concrete wherein diverse waste materials have been converted as valuable spin-offs. This paper presents a wide experimental study on the sustainability of employing waste sawdust as a fine/coarse aggregate replacement incorporating fly ash (FA) and granulated blast furnace slag (GBFS) to make high-performance cement-free lightweight concretes. Waste sawdust was replaced with aggregate at 0, 25, 50, 75, and 100 vol% incorporating alkali binder, including 70% FA and 30% GBFS. The blend was activated using a low sodium hydroxide concentration (2 M). The acoustic, thermal, and predicted engineering properties of concretes were evaluated, and the life cycle of various mixtures were calculated to investigate the sustainability of concrete. Besides this, by using the available experimental test database, an optimized Artificial Neural Network (ANN) was developed to estimate the mechanical properties of the designed alkali-activated mortar mixes depending on each sawdust volume percentage. Based on the findings, it was found that the sound absorption and reduction in thermal conductivity were enhanced with increasing sawdust contents. The compressive strengths of the specimens were found to be influenced by the sawdust content and the strength dropped from 65 to 48 MPa with the corresponding increase in the sawdust levels from 0% up to 100%. The results also showed that the emissions of carbon dioxide, energy utilization, and outlay tended to drop with an increase in the amount of sawdust and show more the lightweight concrete to be more sustainable for construction applications.

Self-compacting concrete (SCC) became a strong candidate for various construction applications owing to its excellent workability, low labor demand, and enhanced finish-ability, and because it provides a solution to the problem of mechanical vibration and related noise pollution in urban settings. However, the production of Portland cement (PC) as a primary constituent of SCC is energy-intensive, contributing to about 7% of global carbon dioxide (CO₂) emissions. Conversely, the use of alternative geopolymer binders (GBs) in concrete can significantly reduce the energy consumption and CO₂ emissions. In addition, using GBs in SCC can produce unique sustainable concrete with unparallel engineering properties. In this outlook, this work investigated the development of some eco-efficient self-compacting geopolymer concretes (SCGCs) obtained by incorporating different dosages of fly ash (FA) and ground blast furnace slag (GBFS). The structural, morphological, and mechanical traits of these SCGCs were examined via non-destructive tests like X-ray diffraction (XRD) and scanning electron microscopy (SEM). The workability and mechanical properties of six SCGC mixtures were examined using various measurements, and the obtained results were analyzed and discussed. Furthermore, an optimized hybrid artificial neural network (ANN) coupled with a metaheuristic Bat optimization algorithm was developed to estimate the compressive strength (CS) of these SCGCs. The results demonstrated that it is possible to achieve appropriate workability and mechanical strength through 50% partial replacement of GBFS with FA in the SCGC precursor binder. It is established that the proposed Bat-ANN model can offer an effective intelligent method for estimating the mechanical properties of various SCGC mixtures with superior reliability and accuracy via preventing the need for laborious, costly, and time-consuming laboratory trial batches that are responsible for substantial materials wastage.

This research investigated the application of epoxy resin polymer as a self-healing strategy for improving the mechanical and durability properties of cement-based mortar. The epoxy resin was added to the concrete mix at various levels (5, 10, 15, and 20% of cement weight), and the effectiveness of healing was evaluated by microstructural analysis, compressive strength, and non-destructive (ultrasonic pulse velocity) tests. Dry and wet-dry conditions were considered for curing, and for generating artificial cracks, specimens at different curing ages (1 and 6 months) were subjected to compressive testing (50 and 80% of specimen’s ultimate compressive strength). The results indicated that the mechanical properties in the specimen prepared by 10% epoxy resin and cured under wet-dry conditions was higher compared to other specimens. The degree of damage and healing efficiency index of this particular mix design were significantly affected by the healing duration and cracking age. An optimized artificial neural network (ANN) combined with a firefly algorithm was developed to estimate these indexes over the self-healing process. Overall, it was concluded that the epoxy resin polymer has high potential as a mechanical properties self-healing agent in cement-based mortar.

The seismic performance of RC columns can considerably be improved through the use of continuous spiral reinforcement in terms of ductility and energy dissipation capacity. Since the beam-column connections were subjected to brittle failure after earthquakes, the simultaneous application of this method in both beams and columns could greatly improve the seismic behaviour of such connections. In this investigation, a new proposed detail for beam to column connection introduced as "twisted opposing rectangular spiral" was investigated both experimentally and numerically and its seismic performance was verified through comparison with normal rectangular spiral and conventional shear reinforcement systems. In this research, three full scale beam to column connections were first designed according to Eurocode (EC8-04) for Medium ductility classes and then tested by quasi-static cyclic loading suggested by ACI Building Code (ACI 318-08). Finally, numerical methods were hired to validate the experimental results. The results indicated that the ultimate lateral resistance, ductility and energy dissipation capacity of the connection could be improved using the new proposed connection.