3D concrete printing technology has considerably progressed in terms of material proportioning and properties; however, it still suffers from the difficulty of incorporating steel reinforcement for structural applications. This paper aims at developing a modular approach capable of manufacturing 3D printed beam and column members reinforced with conventional steel bars. The cubic-shaped printed modules had 240 mm sides, possessing four holes on the corners for subsequent insertion of flexural steel and grouting operations. The transverse steel (i.e., stirrups) was manually incorporated during the printing process. The reinforced 3D printed beams were built by joining the various modules using high-strength epoxy resins. Test results showed that the compressive and flexural strengths of plain (i.e., unreinforced) 3D printed specimens are higher than traditionally cast-in-place (CIP) ones, which was mostly attributed to the injected high-strength grout that densifies the matrix and hinders the ease of crack propagation during loading. The flexural moment capacity of 3D reinforced printed beams were fairly close to the ACI 318-19 code provisions; however, about 22% lower than companion CIP members. The reduction in peak loads was attributed to the modular approach used to construct the 3D members, which might alter the fundamentals and concepts of reinforced concrete design, including the transfer and redistribution of stresses at ultimate loading conditions.
In offsite construction, various factors contribute to variability in cycle times at workstations in production facilities, leading to imbalanced production lines. Understanding these factors is vital for implementing Heijunka, a fundamental lean principle that consists of levelling out the work schedule. This study presents a qualitative approach for identifying and understanding factors that influence variable cycle times at the workstation level. The application of the approach is demonstrated in reference to a semi-automated framing workstation in a panelised construction facility. A list of 36 potential influencing factors categorised into eight classes is first compiled based on observation of the process, a cross-functional diagram, and a review of relevant studies, and then discussed based on feedback solicited from personnel at the case framing station through a semi-structured interview. The approach, its application, and the results demonstrate the effect of expending effort on the identification and understanding of cycle time-influencing factors in improving the accuracy of cycle time analysis, thereby facilitating the implementation of Heijunka.
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