物理信息引导的3D打印混凝土智能设计方法OA

Introduction 3D printing technology offers a significant potential in the construction sector via enhancing build efficiency and enabling greater design freedom.However,its widespread adoption faces challenges related to the multi-objective optimization of concrete performance and the lack of intelligent decision-making for printing parameters.To address these issues,this paper was to propose a hybrid modeling framework that could integrate physics-informed and data-driven approaches for the intelligent design of 3D printing parameters tailored to target material properties.The framework first developed a rheological performance prediction model based on the physical information equation(PIE).This model,combined with the PIE,was then used to construct an extrusion screw speed(ES)database.The framework could enable accurate determination of the printing speed(PS)via incorporating key parameters such as the target concrete flow rate(Q),printing nozzle diameter(PN),and printing layer height(PH).The proposed intelligent parameter design method could provide a quantitative tool for the collaborative control of multiple concrete properties,significantly enhancing the efficiency and reliability of process parameter optimization.This study could offer a theoretical foundation and methodological support for improving the build quality and process stability of 3D printing concrete,thus facilitating the technology's application and adoption in construction engineering. Methods A hybrid physics-data driven framework was developed for printing parameter optimization.This involved two distinct fusion strategies: 1.Physics-Informed Rheological Prediction("PIE embedded in ML"):A Physics-Informed Convolutional Neural Network(PICNN)was constructed to predict rheological parameters(i.e.,Yield Stress-YS,and Plastic Viscosity-PV).Physical equations describing the relationship between mix proportions and rheology were embedded as soft constraints into the CNN's loss function.The Dung Beetle Optimizer(DBO)was used for hyperparameter tuning,including the weights balancing data-driven loss and physics-informed loss. 2.Physics-Serialized Extrusion Speed Prediction("PIE serialized with ML"):An ES prior database was built via coupling multiple physical equations(i.e.,density,Bernoulli,torque)with the predicted rheological parameters from the PICNN model.A Random Forest(RF)model,optimized using DBO,was then trained on this database to predict ES intelligently based on material properties and equipment conditions. Based on the principle of mass conservation,an explicit calculation equation for PS was derived as a function of Q,PN,and PH.This could complete the parameter optimization chain from material properties to process parameters. Results and Discussion The PICNN model for predicting YS and PV demonstrates a stable convergence during training.Compared to the pure data-driven CNN model,the PICNN achieves significantly lower losses(e.g.,testing set average loss for YS:PICNN 198.92 Pa vs.CNN 307.80 Pa;for PV:PICNN 0.37 Pa·s vs.CNN 0.77 Pa·s),confirming that physical constraints enhance predictive accuracy and robustness.Furthermore,the predicted values from PICNN show a high consistency with the calculations from the pure PIE(e.g.,low MAPE of 6.02%for YS and 2.60%for PV on testing sets),thus proving a great adherence to physical laws. The RF model for ES prediction,trained on the physics-generated database,achieves a high accuracy with R2 values of 0.99(training set)and 0.97(testing set),and RMSE values of 1.86 r/s(training)and 2.39 r/s(testing).The scatter plots show points closely distributed around the fit line,and error distributions are concentrated at low levels,indicating the model's effectiveness in learning the complex relationships for ES prediction. The derived equation for PS effectively establishes the quantitative relationship with Q,PN,and PH.A case study presenting calculated PS values for various common combinations of these target parameters provides a practical reference for configuring printing processes,demonstrating the applicability of the complete optimization framework. Conclusions This study established a comprehensive physics-data hybrid modeling framework for optimizing 3D printing concrete parameters,achieving a precise matching between parameters and material rheological properties. The physics-informed rheological prediction model(PICNN)significantly improved prediction accuracy and physical consistency for YS and PV compared to purely data-driven or purely physics-based approaches. The physics-serialized extrusion speed prediction method,utilizing a physically-consistent database and an optimized RF model,enabled high-precision,robust intelligent recommendation of ES based on material attributes. The complete parameter optimization solution,integrating rheological prediction,ES prediction,and PS calculation,provided a systematic and intelligent paradigm for 3D printing parameter design,reducing reliance on conventional trial-and-error methods. This research could contribute to enhancing the forming quality and process stability of 3D printing concrete,facilitating the intelligent development of construction 3D printing technology.Future work could focus on more complex multi-objective optimization scenarios and real-time dynamic parameter control strategies.