Simulation-Based Evaluation of Lean Robotics for Construction-Scale Robotic 3D Printing in Construction Automation

The construction sector faces persistent challenges in productivity, workforce availability, and safety, motivating the adoption of robotics, automation, and digitization across planning and execution. Within construction-scale robotic 3D printing, current research aligned with lean robotics emphasizes digital design, robotic automation, and process optimization. However, simulation is still used for verification of predefined solutions rather than for comparative evaluation of alternative design and execution strategies. This limitation highlights the need for a simulation-based research framework for construction-scale robotic 3D printing that connects computational design to robotic execution through a reproducible digital thread evaluation loop. The proposed method operationalizes two execution-critical constructs for construction automation: agility, defined as the speed of reacting to changes in design or process conditions, and adaptability, defined as the breadth of design variants that remain executable under robotic constraints. Building on these measures, a closed-loop improvement mechanism is introduced that combines 1-neuro-symbolic optimization for multi-objective exploration under hard constraints with 2-Theory of Inventive Problem Solving-Negation (TRIZ-N) reasoning. TRIZ-N is used as an evidence-triggered contradiction-breaking layer that performs structured assumption negation when optimization saturates on a Pareto front, generating new design and process transformations that expand the feasible solution space. The resulting framework supports transparent peer review by providing explicit parameterization, Key Performance Indicator (KPI) computation methods, and a transferable procedure for robot-oriented design and digitized data flow from geometry to collision-free robotic execution.