A Strut and Tie Neural Network Surrogate for Failure-State Prediction of Concrete D-Regions Designed by the Compatible Stress Field Method

The Compatible Stress Field Method (CSFM) designs the discontinuity regions (D-regions) of structural concrete, such as deep beams, hammerhead piers and pile caps, by combining a lower-bound stress field with kinematic compatibility and realistic constitutive behaviour, but its nonlinear solution procedure is too slow for the repeated evaluations that design-space exploration, reinforcement optimisation and reliability analysis demand. This study presents a neural-network surrogate that, in a single forward pass, maps a normalised description of a D-region design to its failure load factor and to the strut and tie (STM) member forces at the failure state, so the surrogate returns an interpretable and nearly statically admissible force state rather than an opaque capacity number. Training data come from sweeping an existing reference solver over a Latin Hypercube design of experiments across four D-region archetypes: deep beam, hammerhead pier, multi-column bent and pile cap. Trained per archetype, the surrogate predicts the failure load factor with a coefficient of determination of 0.96-0.99 and a mean absolute percentage error of 3-6 %, ahead of a panel of seven tabular baselines, and reproduces the reference member forces to within about 6 % in relative error (coefficient of determination above 0.99), with a nodal-equilibrium residual of about 1.5 % of the applied load. A bagged deep ensemble with split-conformal calibration attaches a prediction interval with a finite-sample coverage guarantee to each output, and a domain-of-validity flag built on the ensemble spread catches the majority of out-of-domain designs, on which accuracy degrades as expected. The reference solver is itself checked against an experimental pier-cap benchmark, completing a two-tier validation, though that tier rests on a single five-specimen series and inherits a systematic conservative bias. Against the reference solver the surrogate is between one and nearly four orders of magnitude faster, depending on the archetype and on batching, which makes the design-space exploration, reinforcement optimisation and reliability analysis of D-regions tractable.