In recent years, the use of Wire-and-Arc Additive Manufacturing (WAAM) for strengthening standardized steel elements received significant interest within the research community. The reason for this lies in the theoretical potential of WAAM to improve the economic and environmental aspects of contemporary steel construction through efficient material consumption. As efficiency is often obtained through detailed design study, the paper presents a design exploration of suitable stiffener geometries under the assumption of infinite geometrical freedom. The assumption is eventually invalidated as process constraints specific to the generated geometries emerge from test trials. Once identified, process constraints are documented and overcome through adequate and precise path planning. Feasibility analysis is an important step between design and fabrication, especially in the case of large-scale or geometrically complex components. With reference to the case of stiffeners, a feasibility analysis is necessary to take into account the specific geometrical limits of the build volume, which is not typically the case for conventional WAAM fabrication. The current research provides the first investigation to understand the means for future on-site WAAM strengthening of existing steel structural elements.

Traditional reinforcement cages are manufactured in a handicraft manner and do not use the full potential of the material, nor can they map from optimised geometries. The shown research is focused on robotically-manufactured, structurally-optimised reinforcement structures which are prefabricated and can be encased by concrete through SC3DP in a combined process. Based on the reinforcement concept of “reinforcement supports concrete,” the prefabricated cages support the concrete during application in a combined AM process. To demonstrate the huge potential of combined AM processes based on the SC3DP and WAAM techniques (for example, the manufacturing of individualized CPS), the so-called FLOWall is presented here. First, the form-finding process for the FLOWall concept based on fluid dynamic simulation is explained. For this, a three-step strategy is presented, which consists of (i) the 3D modelling of the element, (ii) the force-flow analysis, and (iii) the structural validation in a computational fluid dynamics software. From the finalized design, the printing phase is divided into two steps, one for the WAAM reinforcement and one for the SC3DP wall. The final result provides a good example of efficient integration of two different printing techniques to create a new generation of freeform coastline protection structures.

Machine learning ($ML$) has been proven effective in various scientific and industrial domains. Nevertheless, its practical application in the construction industry requires further investigation. Leveraging ML capabilities conserves human resources, reduces errors, and speeds up computation and interpretation tasks. The efficacy of ML algorithms depends on factors like ability, result accuracy, analysis cost, and sensitivity to parameter count and available data volume. This study explores the potential of using ML to delegate structural calculation processes, which is an aspect with limited attention. Concrete beam section calculations based on the American Concrete Institute ($ACI$) standards were chosen as a case study to assess ML’s capacity to emulate a structural designer’s role. Initially, manual design steps and standard considerations for a concrete beam section were parametrically coded in MATLAB. Validation against structural design references ensured code accuracy in calculating shear and bending capacities. The parametric results served as initial data (lookup table) for training ML operators. Various ML techniques, including fuzzy logic ($FL$), neural network ($NN$), and adaptive neuro-fuzzy inference system ($ANFIS$), were coded in MATLAB. A comparative analysis of the three ML operators assessed their performance in replacing standard calculations. Parametric examples illustrated each operator’s precision in delegation compared to direct calculations. The study also explored the impact of the number of parameters and lookup table size on the accuracy of each ML operator. The findings revealed that while all three operators could delegate standard calculations, their precision varied. Notably, when the lookup table was optimal, $ANFIS$ operators demonstrated the ability to represent standard calculations with varying parameter counts and high precision. Focused on beam calculations, this study provides insights into ML operator performance. The outcomes, including selecting the most capable operator and their sensitivity to parameters and lookup table size, offer valuable guidance for researchers interpreting experimental and numerical analysis results.

Machine Learning (ML) has proved its capabilities in different scientific and industrial fields, but it needs to be further investigated in the construction industry for practical utilization. One of the use cases of ML is delegating the structural calculation process. In this study, to discuss ML’s capabilities in performing the work of a structural designer, calculations of concrete sections based on ACI (American Concrete Institute) as a case study were selected. At first, all manual design steps and standard considerations for a concrete beam section were coded parametrically in MATLAB. After comparing with structural design references to prove the accuracy of codes in calculating shear and bending capacities, the parametric results were used as initial data (Look−up table) for training the ML operators. Regarding different types of ML techniques and as a comparison between them, in the next steps, all essential codes for Fuzzy Logic (FL), Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were coded in the same platform. The performance of the three coded ML operators to replace (delegate) standard calculations compared to direct calculations was individually investigated and displayed through parametric examples. After initial examples, the influences of the number of parameters and size of the Look−up table on the accuracy of each operator were discussed. The study concluded that although all three operators can delegate the standard calculation, the precision of the results differs considerably. In case of the desirable size of the Look−up table, ANFIS operators can represent the standard calculations with a different number of parameters and entirely high precision.