As a predominant construction material in modern civil infrastructure (i.e., roads, bridges, ports, and airports), concrete is susceptible to adversing actions such as external loading, freeze–thaw cycling, and salt ingress. These factors readily induce surface spalling and scaling, accelerating service-life deterioration and posing significant safety risks. The existing polymer-modified mortars and epoxy-based systems are commonly employed for the repair and strengthening of concrete structures. An organically modified belite-calcium sulfoaluminate (HB-CSA) cement mortar is adopted in concrete repair projects due to its rapid setting and early strength. However, the performance requirements differ markedly across construction scenarios in low-flow applications (e.g., vertical or overhead placement), the mortar must maintain a low flowability to prevent sagging/run-off during placement, and mechanical performance depends primarily on bond strength, with relatively relaxed demands on compressive and flexural strengths. By contrast, high-flow applications (e.g., pavement repair or large-area casting) require a high flowability to ensure adequate spreading and filling workability. The compressive strength is a principal mechanical target, while the flexural and bond performance must be also satisfied. These divergent demands make it difficult for a single HB-CSA mixture to meet the performance needs of multiple repair scenarios, underscoring the practical importance of multi-scenario mixture-design optimization for HB-CSA repair mortars.

This study was to investigate an organically modified belite-calcium sulfoaluminate (HB-CSA) cement mortar system. Flow spread was employed as a constraint to distinguish high-and low-flow-state scenarios, while 7-d compressive, 7-d flexural, and 7-d bond strengths were taken as the optimization objectives. To address mixture optimization for both flow states, we introduced (i.e., pioneering its use in the cement materials field) a variational-autoencoder-based generative optimization algorithm (VAE-GOA). The method could leverage a generative model to progressively estimate the probability distribution of optimal mixtures over the global design space. In parallel, an adaptive global-exploration strategy prioritized high-potential regions to mitigate premature convergence to local optima, and an iterative optimization scheme further drived the search toward superior solutions. The approach delineated the optimal composition windows and corresponding performance of HB-CSA repair mortars under different flow-state scenarios via visualizing the VAE-GOA–estimated distribution of optimal mixtures. In addition, backscattered electron (BSE) imaging of the interfacial transition zone (ITZ) was also employed to elucidate the mechanisms underlying the improvement in bond performance.

At the outset, the VAE-GOA conducts broad, globally exploratory sampling, while unavoidably covering some low-performing regions, effectively uncovering previously unexplored high-potential areas. As iterations proceed, probability mass progressively concentrates in high-performance regions. The search distribution transitions smoothly from global exploration to local exploitation, reduces attention to low-performing zones, and converges toward a compact subspace containing the best-performing mixtures. These dynamics substantiate the algorithm's intended explore-then-exploit behavior in a multi-constraint design space and effectively mitigate premature convergence to local optima.

Based on two optimization generations and despite strict flowability constraints and limited sampling, the VAE-GOA delivers both macro-level performance gains and a marked expansion of feasible design space. The optimum weighted comprehensive performance is improved by about 20% in the high-flow state group and by >25% in the low-flow state group. The candidates with compressive strength >50 MPa increase from 2 to 6, and those with bond strength >6 MPa increase from 4 to 12, substantially broadening mixture options that meet key targets. For low-flow state scenario, increasing the USCMs is accompanied by a nonlinear decrease in the HPMC, and higher USCMs generally require a lower water–binder ratio (w/b). A practical window of 15%–30% USCMs, 2%–6% Wacker 328, and about 0.1% HPMC at a w/b ratio of 0.22 achieves a low flowability witha high interfacial performance (i.e., mixture L8 attains 48.6 MPa (compressive), 9.2 MPa (flexural), and 6.5 MPa (bond)). For high-flow state scenario, the strength-oriented optimum occurs near about 5% USCMs + 1% Wacker 328 at a w/b ratio of 0.25 (i.e., H7 reaches 63.4 MPa (compressive), 8.3 MPa (flexural), and 4.5 MPa (bond)). For cost-oriented deployment, increasing the USCMs to ~30% with 5% Wacker 328 at a w/b ratio of 0.20 yields a balanced, economical option (i.e., ≈50.7/7.4/6.6 MPa for compressive/flexural/bond). Overall, substituting 15%–30% the USCMs for HB-CSA regulates a flowability and enhances a bond strength as well as reduces production costs by 13%–27%.

Holding the HPMC and w/b ratio roughly fixed while pushing the USCMs beyond 30% (H8 vs. H1) reduces the compressive strength by 14.7 MPa but elevates the bond strength to 6.9 MPa, highlighting a deliberate bond-first Pareto choice. The backscattered electron imaging corroborates this pheoneman. The interfacial transition zone (ITZ) in H8 exhibits a significantly lower porosity and a denser microstructure at the old–new interface, explaining the observed bond enhancement.

In this study, the VAE-GOA could expedite the discovery of high-potential HB-CSA mixtures under strict flow constraints and limited sampling. The comprehensive performance of high-flow state mortars was increased by 20%, and that of low-flow state mortars by >25%. Beyond single best points, the method could yield scenario-specific composition windows and a diverse portfolio choices that practitioners could select from according to the strength, bond, and cost priorities. In particular, low-flow applications were supported by a stable window (e.g., 15%–30% USCMs with 0.1% HPMC and w/b ≈ 0.22), while high-flow placement admited both a strength-oriented option (~5% USCMs + 1% Wacker 328 at w/b = 0.25) and a cost-oriented alternative (30% USCMs + 5% Wacker 328 at w/b = 0.20). The VAE-GOA could be sample-efficient, resilient to local optima, and readily extensible to multi-objective constraints. Looking ahead, coupling the framework with durability targets (e.g., freeze–thaw and chloride ingress), long-term field validation, and uncertainty-aware priors could broaden its applicability to scenario-aware specification and lifecycle-optimized repair design.