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.

The efficient utilization of robotic construction of structures requires a review of structural material, elements and details, while traditional construction methods face difficulties in producing some dry concrete joints, robotic methods can manufacture them efficiently. With the rapid advancement of new construction methods, more effort should be directed towards developing new types of joints manufactureable by Additive Manufacturing (AM) methods. Accordingly, considering the potentials of robotic techniques, this article proposes eight new concrete dry joints. Concrete portal frames with two identical joints at the end of the beam are cast and evaluated under bending and shear. The performance of each joint, evaluated by experimental and numerical studies based on the crack distribution and stresses, was discussed. Furthermore, various aspects of the process of robotic subtractive manufacturing of the connections are discussed through the selected printed/milled geometries; likewise, geometrical accuracy by microscanning was measured, proving this technique’s capability (methodology). Based on findings, some proposed joints that can be manufactured entirely and quickly by robotic additive and subtractive techniques structurally can produce the full strength of the monolithic reference section. The range of the capacities and initial stiffness is, accordingly, [50%, 106%] and [51%, 124%] of the monolithic elements.

The study described in this contribution contains a fundamental strategy to select geometries for dry joint profiles in 3D-printed concrete constructions. A database, here called the ‘joint catalogue’, contains a variety of joint types adapted from timber, steel, and bionic connections. Weighting factors and different criteria evaluate and score the various joint profiles (e.g., manufacturability, duration of manufacturing, and mechanical behaviour). Therefore, an algorithm sums up the scores leading to the preselection of better suitable profiles. The preselected joint profiles were afterwards analysed by the finite element method, determining the load capacity of the joint in a unit specimen. According to the joint catalogue, a smooth, triangular, truncated cone and arc joint profile appeared to be the optimal combination for dry joints in additive manufacturing of construction (AMC).

This study explores the potential use of new connections to shape precast building geometries, focusing on connection performance, robotic fabrication, and foldable structural elements. Three connection types, including coupled-bolts, hinges, and steel tubes, were initially proposed and assessed in beam and portal frame geometries. In contrast, the study introduces conceptual ideas; initial experimental and numerical studies were conducted to estimate connection capacities. Robotic fabrication for connecting elements to reused concrete and converting floor elements into beams was detailed, showcasing robotic technology’s performance and potential. These connections were employed in designing new precast element geometries, ranging from simple beams to multi-story buildings. Geometric properties and volume quantities of folded and opened geometries were studied using 37 CAD models. To properly discuss the joint performance reference, monolithic elements with exact dimensions were created for comparison. Despite varied connection capacity ($38\%$ to $100\%$), the steel tube exhibited the most desirable performance, resembling a monolithic element with an exact size. Some proposed foldable geometries showed a significant reduction (up to $7\%$) in element dimensions to facilitate transport and construction.

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.

This study addresses the fabrication challenges associated with producing diverse geometries for concrete dry connections, particularly regarding cost, time, and geometric limitations. The research investigates methods for fabricating precise, rebar-free dry connections in concrete, focusing on stamping and green-state computer numerical control (CNC) milling. These methods are evaluated using metrics such as dimensional accuracy, tool abrasion, and energy consumption. In the stamping process, a design of experiments (DOE) approach varied water content, concrete age, stamping load, and operational factors (vibration and formwork) across cone, truncated cone, truncated pyramid, and pyramid geometries. An optimal age range of 90 to 105 min, within a broader operational window of 90 to 120 min, was identified. Geometry-specific exceptions, such as approximately 68 min for the truncated cone and 130 min for the pyramid, were attributed to interactions between shape and age rather than deviations from general guidance. Within the tested parameters, water fraction primarily influenced lateral geometric error (diameter or width), while age most significantly affected vertical error. For green-state milling, both extrusion- and shotcrete-printed stock were machined at 90 min, 1 day, and 1 week. From 90 min to 1 week, the total milling energy increased on average by about 35%, and at one week end-face (head) passes caused substantially higher tool wear, with mean circumference losses of about 3.2 mm for head engagement and about 1.0 mm for side passes. Tool abrasion and energy demand increased with curing time, and extrusion required marginally more energy at equivalent ages. Milling was conducted in two engagement modes: side (flank) and end-face (head), which were evaluated separately. End-face engagement resulted in substantially greater tool abrasion than side passes, providing a clear explanation for tolerance drift in final joint geometries. Additionally, soil-based forming, which involves imprinting the stamp into soft, oil-treated fine sand to create a reversible mold, produced high-fidelity replicas with clean release for intricate patterns. This approach offers a practical alternative where friction and demolding constraints limit the effectiveness of direct stamping.