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Self-healing construction materials mix-design database: 1,257 formulations extracted from 161 peer-reviewed publications

Year: 2026
Type: dataset
Abstract: A database of 1,257 unique self-healing construction-material formulations compiled from 161 peer-reviewed publications (2013-2026). Each row is one distinct formulation as reported in one source, with every populated cell traceable to a publication that states it. Three matrix families are stacked in one table and distinguished by the material_class field: Portland-cement concrete, mortar and paste (913 formulations from 116 sources); asphalt, bitumen and bituminous mixtures (330 from 43); and alkali-activated and geopolymer binders (14 from 2). The 210 fields comprise a 105-field spine shared by all three families plus three disjoint matrix-specific extension blocks (34 opc_*, 39 asp_*, 25 aam_* fields) populated only for their own class. The spine covers formulation identity; matrix composition; healing-system identity and dosage; processing and curing; the damage protocol (crack-generation method, crack age at damage, pre-crack load level, measured crack width and the method used to measure it, depth, through-crack status, damage cycles); the healing exposure (regime, duration, temperature, relative humidity, wet-dry cycle definition, water chemistry, applied stimulus); healing performance; mechanical performance with test ages; and per-row provenance. Users modelling from these data should cluster by source publication, since formulations from one study are not independent replicates: one source contributes 80 rows, and 74.6% of occupied cells in the Portland-cement design space rest on a single publication. Analyses that pool formulation rows recover ten significant cross-study contrasts of twelve; none of the ten tested at study level survives once the study is the unit of independence. Released with a field-by-field data dictionary giving the type, unit, measured completeness and vocabulary tokens of every column; a controlled-vocabulary reference documenting the 45 closed vocabularies over 453 tokens, the healing-mechanism classification rule and the permitted and forbidden derivations; and a source corpus giving venue, year, first-author country, open-access status and row contribution for each of the 161 contributing publications.
DOI: 10.17632/4vp89whh3h
Institution: Karunya University
Open Access status: green
OpenAlex ID: W7212370076
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