Effects of binder proportion and curing condition on the mechanical characteristics of volcanic ash- and slag-based geopolymer mortars; machine learning integrated experimental study

[Display omitted] •Volcanic ash-ground granulated blast furnace slag mortars are introduced and tested.•Three different RT, OR, and RO curing conditions and temperatures are investigated.•VA-GGBFS thresholds for outperforming RT to OR, and RO samples are determined.•SEM-EDS investigations show formation of N-A-S-(H), N-(C)-A-S-H, and C-A-S-H gels.•Machine-learning models are proposed for the UCS and shrinkage of VA-GGBFS mortars. Contradicting natures of volcanic ash (VA)- and ground granulated blast furnace slag (GGBFS)- based geopolymers with regard to their need for being exposed to a heating source for conducting alkali-reactions make meaningful differences in the strength and flowability of mortars. To address the impacts of types of the geoploymerizing agent, their proportions, and different curing regimes on their strength and shrinkage behavior, three different curing scenarios including curing at room temperature, RT curing; resting under room temperature for various curing periods, and then hydrothermally curing at an elevated temperature for one day, RO curing; and curing in the oven for one day and then resting at the room temperature, for various curing periods, OR curing, are considered. Based on the flowability, setting time, unconfined compressive strength (UCS), and drying shrinkage tests on eight different mixtures, the effects of binder proportion and curing conditions are evaluated. It was observed that with the substitution of 30% VA with GGBFS, specimens cured at RT condition outperformed OR specimens. This threshold was 45% while comparing RT and RO specimens and with higher slag content, RT specimens gain higher UCS. Also, for GGBFS proportion less than 45%, specimens cured at RO conditions showed higher long-term UCS compared to the OR specimens, and this behavior was reversed for higher GGBFS content. Scanning electron microscopy test results indicated that N-A-S-(H) type gel is the dominant geopolymeric gel in VA-based specimens, and as the amount of slag increases, the gels formed change to N-(C)-A-S-H and with further addition of GGBFS, C-A-S-H type gel is the dominant gel which results in strength development in GGBFS-based specimens. Based on the results of 80 UCS and 140 drying shrinkage tests, different machine learning (ML) algorithms and an evolutionary model were employed to present accurate and cost-effective predictive models as useful and capable methods for practitioners and researchers for the preliminary design sta