Buildability analysis on squared profile structure in 3D concrete printing (3DCP)

Abstract

Numerical modelling and simulation approaches can be used to optimize material combinations, structural design, and process parameters to achieve the desired structural performance of 3D-printed structures. In this study, a novel construction and demolition waste-based mortar mixture was prepared for the 3D concrete printing (3DCP) process. A square cross-sectional structure was designed and 3D-printed using a lab-scale gantry-type 3D printer for buildability analysis. The geopolymer material was also characterized to obtain time-dependent properties for use in a numerical model capable of predicting the buildability (final height) of structures. In the numerical modelling and simulation phase, predictive simulations were performed for experimentally 3D-printed structures to validate the predictability of the numerical model. The numerical model revealed a sound approximation of buildability with an error of 6.3% only. Furthermore, using numerical simulations, sensitivity analyses were performed to evaluate the impact of designed height and 3DCP process parameters (i.e., printing speed and layer width) on the buildability of structures. The numerical modelling and simulation results revealed a strong impact of both process parameters (i.e., printing speed and layer width) on the buildability of 3D-printed structures. A maximum buildability of 410.6 mm was achieved for structure 3D-printed at a printing speed of 20 mm/s and layer width of 45 mm. Overall, an improved buildability was observed for lower printing speeds and higher layer widths; however, the buildability performance was more sensitive to the layer width.

Keywords:

• Geopolymer
• concrete 3D printing
• numerical modelling
• simulations
• buildability

1. Introduction

Incorporating 3D printing (3DP) technology in the construction industry can potentially revolutionize the sector, providing a range of advantages over conventional building methods. These benefits include enhanced customization, sustainability, efficiency, and affordability (Al Rashid & Koç, 2023a). By utilizing computer-controlled machinery to deposit successive layers of material, 3DP offers a unique building approach that produces complex three-dimensional structures (Khan & Koç, 2022). In contrast to traditional construction techniques, 3DP allows the fabrication of highly customized and intricate structures that would be challenging, if not impossible, to build otherwise (Comminal, da Silva, Andersen, Stang, & Spangenberg, 2020). Furthermore, 3DP technology can help reduce the environmental effect of building operations by decreasing emissions ­associated with the transportation of raw materials and material wastage (Al Rashid, Ahmed, Khalid, & Koç, 2021). 3DP technology can reduce construction time and labour costs by automating the building process and removing the need for traditional labour-intensive tasks like bricklaying and pouring mortar. These advancements in 3DP technology may also help to make construction more economical (Alhumayani, Gomaa, Soebarto, & Jabi, 2020; Tay et al., 2017). Several instances of utilizing the 3DP process in the construction sector, such as residential buildings, office complexes, and bridges, are documented in previous research (Sai Sandeep & Muralidhara Rao, 2017). Nevertheless, despite these advancements, 3DP technology in construction is still in its early stages of development. Extensive exploration and experimentation are required to fully unlock its potential in enabling novel processes, sustainable projects, and green materials that surpass the limitations of conventional construction techniques.

The construction sector plays a pivotal role in shaping our surroundings; nevertheless, it has been linked to substantial carbon emissions (Labaran, Mathur, Muhammad, & Musa, 2022). One of the primary components in construction, ordinary Portland cement (OPC), has been identified as responsible for 5% of total carbon emissions (Alsalman, Assi, Kareem, Carter, & Ziehl, 2021). Due to the substantial energy demands of OPC production and the escalating carbon footprint, researchers have initiated investigations into alternative eco-friendly substitutes (McLellan, Williams, Lay, Van Riessen, & Corder, 2011). A particularly promising avenue is the adoption of alkali-activated geopolymers instead of conventional OPC (Al-Noaimat, Ghaffar, Chougan, & Al-Kheetan, 2023). Alkali-activated geopolymers are cementitious materials that can be derived from industrial byproducts, such as fly ash (Shen, Li, Lin, & Li, 2023), slag (Vázquez-Rodríguez et al., 2023), and other aluminosilicate-rich materials (Adeleke, Kinuthia, Oti, & Ebailila, 2023). Unlike OPC, the manufacturing process of alkali-activated geopolymers results in fewer carbon emissions and utilizes natural industrial waste materials, thereby reducing the consumption of finite natural resources (Dal Poggetto, Fortunato, Cardinale, & Leonelli, 2023). Furthermore, alkali-activated geopolymers have demonstrated exceptional durability (Srinivasa, Swaminathan, & Yaragal, 2023) and superior thermal characteristics (El Fadili et al., 2023). One of the primary advantages of employing alkali-activated geopolymers is their potential to support circular economy models that benefit various stakeholders (Shehata, Mohamed, Sayed, Abdelkareem, & Olabi, 2022).

One promising approach for producing geopolymers is using construction and demolition waste (CDW) as raw materials. Since CDW is a significant contributor to landfill waste, using CDW-derived geopolymers could offer an environmentally responsible alternative (Khan et al., 2022; Mir et al., 2022; Mir, Khan, Kul, Sahin, Lachemi, et al., 2022; Panizza, Natali, Garbin, Tamburini, & Secco, 2018; Panizza, Natali, Garbin, Ducman, & Tamburini, 2020). Geopolymers derived from CDW showcase properties similar to those of traditional geopolymer materials, for instance, exceptional compressive performance and long-lasting durability. These characteristics depict these materials as well suited for constructing and fabricating buildings and structures. By incorporating CDW as a raw material for geopolymers in 3DP, there is a potential to embrace the concepts of value creation and the circular economy. This approach can reduce fabrication expenses and decrease reliance on virgin materials, promoting sustainability and resource efficiency (Deschamps, Simon, Tagnit-Hamou, & Amor, 2018; Mir et al., 2023; Ulugöl et al., 2021). 3DP is a novel technology that presents various benefits over conventional construction approaches, such as decreased labour costs, lower carbon dioxide emissions, shorter production time, ease of use, and the ability to modify designs as desired (Chougan et al., 2021; Ikram, Al Rashid, & Koç, 2022; Ordoñez, Neves Monteiro, & Colorado, 2022). From an environmental standpoint, 3DP can reduce manufacturing waste, minimize carbon footprints, and support circular economy initiatives. However, the current methodology for 3DP involves trial-and-error experimentation, resulting in costly and time-consuming final product development (Al Rashid & Koç, 2021, 2022, 2023b, 2023c). Moreover, when implementing 3DP at a structural scale, there is a considerable risk of wasting significant resources and time in case of trial-and-error failures (Al Rashid, Ikram, & Koç, 2023; Imran, Al Rashid, & Koç, 2022). The lack of reliable simulation tools for 3D concrete printing (3DCP) results in conservative designs; therefore, to increase the likelihood of successful and cost-effective printing, virtual simulations of the 3DCP process can be employed (Khan & Koç, 2023; Perrot et al., 2021).

Buildability and shape retention refer to the ability of 3D-printed composites to maintain their form and support successive layers without distortion (Şahin et al., 2021). Chougan et al. (2020) examined the buildability of a mixture containing 40 wt% smaller aggregates and 60 wt% larger aggregates, as compared to another mixture, where 60 wt% smaller aggregates and 40 wt% larger aggregates were used in the geopolymer mixture, resulting in improved buildability. The key to buildability is achieving a high yield stress to prevent deformation or collapse, but this yield stress should not be excessively high to hinder the extrusion process (Chougan et al., 2020). According to Şahin et al. (2021), Zhao et al. (2021), Panda, Ruan, Unluer, and Tan (2020), Panda and Tan (2018), and Panda, Unluer, and Tan (2018), the rapid development of static yield stress immediately after layer deposition is crucial for maintaining buildability and preventing structural collapse or deformation. Similarly, research by Ma et al. (2022), Bong, Xia, Nematollahi, and Shi (2021), Muthukrishnan, Ramakrishnan, and Sanjayan (2021), Lv, Qin, Liang, and Cui (2021), and Lim, Panda, and Pham (2018) have shown that a swift recovery of viscosity and yield stress after extrusion leads to better shape retention in the fresh composite. Furthermore, the inclusion of nanoclay as an aggregate within the fresh composite mixture, along with specific aluminosilicate precursors like silica fume and slag, has been found to accelerate yield stress growth and thixotropy, ultimately enhancing the buildability of the fresh composite. However, it is essential to note that an excessive quantity of these ingredients can hinder extrudability (Bong et al., 2022).

Using numerical modelling and simulation offers the advantage of accurate estimations of the structural performance of the 3DCP process. These tools make it possible to minimize material waste, reduce printing and setup time, and wear and tear on equipment (Jayathilakage, Sanjayan, & Rajeev, 2020). Additionally, simulation results can be used to optimize material properties, structural design, and process parameters to achieve desired structural performance (Vantyghem et al., 2020). The implementation of these simulations can assist in predicting the strength and durability of the final product during the 3DCP process. Additionally, numerical modelling and simulation approaches can enhance the design of the 3D-printed built-environment structure by minimizing resource consumption and enhancing its structural performance (de Matos, Foiato, & Prudêncio, 2019; Le et al., 2012). This is particularly critical when constructing buildings with complicated geometries, which can be difficult to construct using traditional construction methods.

A significant research focus is now on utilizing waste materials for construction applications; however, developing geopolymer mixtures from waste materials is still in its infancy. In addition, several challenges prevail in the successful prediction of the 3DCP process, such as identifying the material model, accounting for all the internal/external variables during the actual 3DCP process, and validation of numerical modelling predictions. Given the literature in this field, there is an urgent need to develop and characterize sustainable construction materials for the 3DCP process. In addition, efforts are desired to utilize and improve numerical models for predicting the performance of the 3DCP process. Therefore, a novel CDW-based mortar mixture was prepared for the 3DCP process in this study. A square cross-sectional structure was designed and 3D-printed using a lab-scale 3D printer for buildability analysis of the prepared CDW-based geopolymer material. The geopolymer material was also characterized for time-dependent properties for use in a numerical model capable of predicting the buildability (final height) of structures. In the numerical modelling and simulation phase, predictive simulations were performed for experimentally 3D-printed structures to validate the accuracy of the numerical model. Finally, numerical simulations were performed to evaluate the impact of 3DCP process parameters (i.e., printing speed and layer width) on the buildability of structures.

2. Materials and methods

The overall work is divided into three main sections: the preparation and characterization of CDW-based mortar mixture, design and buildability analysis of squared profile 3DCP structures, the use of numerical modelling approach to predict the buildability designed structures, and finally the sensitivity analysis on the effect of process parameters on resulting buildability of structures.

2.1. Geopolymer mortar preparation

A novel CDW-based geopolymer material was prepared for 3DCP. The geopolymer mixture contained different waste materials, including bricks, roof tiles, glass, concrete, and blast furnace slag. The geopolymer mixture comprised 200 g each of hollow brick, red clay brick, roof tile, blast furnace slag, and 100 g each of glass and concrete per 1000 g of precursors. In addition, the geopolymer mixture contained NaOH with 10 M molarity and 4 wt% of Ca(OH)₂ with water-to-binder and aggregate-to-binder ratios of 0.33 and 0.35, respectively. The details on the preparation of geopolymer mortar with the composition of different constituents are reported by authors elsewhere (Khan et al., 2023). Furthermore, the detailed characterization of the CDW-based geopolymer mortar used in this study is reported in our previous studies (Demiral et al., 2022; Ilcan et al., 2022; Şahin et al., 2021).

2.2. 3D concrete printing process

The 3DCP experiments were performed using a lab-scale gantry-type 3D printer with a build volume of 1 m × 1 m × 0.4 m (length × width × height). The CDW-based geopolymer mortar was prepared and transmitted to the computer numerical control (CNC) router through a pump via a transmission pipe. The CNC router control panel controls the 3DCP process based on the input and designed geometry. The trial runs were also performed before experimentations to ensure a consistent mortar supply and adequate print quality. In this study, a squared cross-sectioned structure measuring 300 mm on each side was designed for buildability analysis of CDW-based geopolymer mortar. A nozzle diameter of 19 mm (which produces a layer with 15 mm height and 30 mm width) and a printing speed of 50 mm/s were selected for the 3DCP process. The designed structure was tested for buildability three times to ensure the repeatability and reproducibility of the 3DCP process for CDW-based geopolymer mortar. The total number of layers successfully 3D-printed before the collapse of the structure was recorded for each experimental run, and overall buildability was calculated.

2.3. 3D concrete printing – numerical modelling

Vantyghem et al. (2020) developed a numerical model for evaluating the predictive performance of the 3DCP process, which was initially tested and validated for conventional concrete materials. However, this study explores and validates the same numerical model for newly developed geopolymer mortar. The numerical model can mimic the 3DCP process for concrete structures for any complex geometry and the impact of different material combinations and 3DP processing parameters (i.e., print speed, layer height, layer width). The overall workflow of the numerical modelling approach, required material properties, and performed numerical simulations are reported in subsequent sections.

2.3.1. Material model for numerical modelling

The numerical model requires material modelling for accurate approximation of the 3DCP process, which includes Poisson’s ratio (ν), Young modulus E(t), density (ρ), cohesion C(t), dilatancy angle (ψ), and angle of internal friction (φ). Wolfs, Bos, and Salet (2018) described how to characterize fresh material properties; therefore, these properties were evaluated through direct shear and unconfined uniaxial compression (UCC) tests. Direct shear and UCC testing were performed per ASTM D3080 and ASTM D2166 standards, respectively. The readers are referred to our previous study for a detailed description of these tests (Khan et al., 2023). The time-dependent material properties evaluated through direct shear and UCC tests and used for numerical modelling are reported in Table 1.

Table 1. Input time-dependent material properties for numerical modelling.

Material property	Symbol	Input data for numerical model	Units
Poisson’s ratio	ν	0.4	–
Young modulus	E(t)	0.0005 × t + 0.0329	N/mm^2
Density	ρ	2.066 × 10^−9	Tonnes/mm³
Cohesion	C(t)	0.00002538 × t + 0.0051628	–
Dilatancy angle	Ψ	∼13	–
Angle of internal friction	φ	0.1074 × t + 57.439	–

2.3.2. Numerical model validation

A structure 3D-printed during the experimentation phase (with a square cross-section and each side 300 mm) was designed using Rhinoceros software. The numerical model from Vantyghem et al. (2020) was used to convert the designed structure into a voxel mesh for analysis. The “Voxel Print” plugin developed for Grasshopper was used for this purpose, where designed geometry can be converted to voxel mesh with designated material properties and 3DCP process parameters. The structure for the 3DCP process was designed for a total height of 300 mm. The characterized time-dependent material properties (reported in Table 1) were assigned to the designed structure, with a printing speed of 50 mm/s, layer height of 15 mm, and layer width of 30 mm, as per 3DCP experiments. The voxel size was kept per the layer height used in the 3DCP experimentation (i.e., 15 mm), and the overall job was set to be completed in 40 steps (i.e., each step will correspond to half of the 3D-printed layer). Finally, an input file (.inp) was generated for analysis in ABAQUS software. The number of steps completed before the structure failure reflected the predicted buildability.

2.3.3. Sensitivity analysis – effect of design height and process parameters

After the numerical model validation for newly developed CDW-based geopolymer mortar, the sensitivity analysis was performed to observe the impact of different designed heights and process parameters on the buildability of structures. In the first step, the structure for numerical simulations was designed for different heights (i.e., 300 mm, 450 mm, and 600 mm) to observe its effect on the buildability. In the subsequent step, the effect of two significant process parameters (i.e., printing speed and layer width) was observed on the buildability performance of 3D-printed structures. Three printing speeds (20 mm/s, 50 mm/s, and 80 mm/s) and layer widths (15 mm, 30 mm, and 45 mm) were selected for investigation. Nine numerical model simulations were performed for each combination of printing speed and layer width.

3. Results and discussion

3.1. 3D concrete printing experiments

After preparing the CDW-based geopolymer mixture, 3DCP experiments were performed to fabricate the squared cross-sectional structures using the equipment reported in section 2.2. Three 3DCP experimental runs were performed to analyse the buildability of the designed structure for the selected set of process parameters (nozzle size – 19 mm, layer height – 15 mm, layer width – 30 mm, and printing speed – 50 mm/s) to eliminate any external or internal error and to ensure the repeatability of results. The buildability is quantified in terms of the successful number of layers 3D-printed before the structure fails, as reported in Table 2.

Table 2. 3DCP and numerical simulation results for buildability of structures.

Analysis type	No. of layers/steps	Buildability (mm)	Error
3DCP experiments	13	200.0	∼6.3%
	14
	13
Numerical simulations	25	187.5

For three experiments, 13, 14, and 13 layers were recorded before failure, resulting in an average of 13.33. The average buildability of the designed structure was therefore recorded as 200 mm. Figure 1 presents the 3D-printed structure at different stages during the 3DCP process. From the experimental campaign, developed novel geopolymer materials were found suitable for 3DP applications in the built environment.

Figure 1. Numerical model validation for buildability of 3DCP geopolymer structure. 3DCP experiment (left) numerical simulation results (right) after (a) 4 layers, (b) 8 layers, and (c) 13 layers.

3.2. Numerical model validation

The time-dependent material properties were used to perform the numerical simulations for the 3D-printed structures. The careful selection of voxel size is vital to achieving an accurate prediction of the buildability of structures. Therefore, the selected voxel size must be a multiple of the selected layer height and designed structure. A voxel size of 15 mm was selected per layer height used during the 3DCP process. A similar set of process parameters (nozzle size – 19 mm, layer height – 15 mm, layer width – 30 mm, and printing speed – 50 mm/s) used in 3DCP experiments were used to perform the numerical simulations. The numerical model predicted the buildability of 187.5 mm for the designed structure. The predicted buildability agreed well with the experimental results, with an error of 6.3%, as reported in Table 2. It is worth noting that the numerical model also estimated the failure behaviour of the 3D-printed structure failure precisely, as reported in Figure 1.

The numerical model underestimated the buildability of the designed structure, which can be attributed to numerical approximations. One of the factors contributing to discrepancies between the numerical model predictions and 3DP results is vibration in the 3DP system, which is not accounted for in the numerical model. The geopolymer material used in the 3DCP process is thixotropic, which exhibits reduced viscosity and flow over time under mechanical stress. As such, handling and vibration during real-time printing can alter the material’s predicted properties, affecting the accuracy of calculated fresh material properties. The experimental methods employed to measure these properties, namely UUC and direct shear testing, involve significant materials handling that can further impact material behaviour. Hence, the impact of mechanical stress, including vibrations from the printing machine and other sources, can have a notable influence on the characteristics of the printed materials. This effect can result in deviations from the anticipated properties of fresh material.

An additional element that plays a role in the disparity of outcomes and aids in the early detection of failures in the numerical model is the cohesive nature of the geopolymer mixture based on CDW. This particular property can potentially enhance the performance of 3D-printed structures by facilitating the combination of materials and successive layers. As a result, it can help prevent issues like buckling and collapse. Furthermore, the variations observed in the numerical model results may be due to the consideration of cement-based materials in the developed analytical approaches. Preparing specimens for the experimental tests, specifically the direct shear and UCC testing, involved manual handling. Meanwhile, the 3D-printed layers were produced using a pressurized pump, which enhances compaction and increases the load-bearing capacity. These factors may have led to deviations in the numerical model predictions from the experimental outcomes. Furthermore, the numerical model assumes perfect contact and alignment of the 3D-printed layers, which does not accurately reflect real-time conditions. Nonetheless, the overall accuracy of the system remains satisfactory, allowing for buildability predictions with a safety margin. However, it should be noted that results can vary depending on the specific machine and material being used.

3.3. Sensitivity analysis

With the satisfactory prediction performance of the numerical model, further numerical simulations were performed to observe the effect of designed structure height and 3DCP process parameters and discussed in the subsequent sections.

3.3.1. Designed height for simulation

Numerical simulations, first of all, involve the design of the desired 3D structure for the 3DCP process. Although the buildability of structures varies under different processing conditions and material properties, the authors investigated the effect of designed structure height on the buildability of 3D-printed structures. For this purpose, the numerical simulations were performed for three different designed heights (i.e., 300 mm, 450 mm, and 600 mm), and all other 3DCP process parameters were selected per experiments performed. The numerical model predicted the buildability of 187.5 mm, 187.6 mm, and 187.7 mm for designed heights of 300 mm, 450 mm, and 600 mm, as reported in Table 3. The numerical model predictions revealed that the input design height did not affect the buildability prediction of 3D-printed structures. The three measurements were in very close proximity and also agreed well with the experimental results. For further investigation on the effect of process parameters on the buildability of 3D-printed structures, discussed in the subsequent section, the designed height was kept constant as it was not found significant on the numerical model predictions.

Table 3. Sensitivity analysis on effect of designed height on buildability.

Analysis type	Designed height (mm)	No. of layers/steps	Buildability (mm)
3DCP experiments	–	13.33	200.0
Numerical simulations	H300	25.00	187.5
	H450	25.01	187.6
	H600	25.02	187.7

3.3.2. 3DCP process parameters

The optimum selection of process parameters during the 3DP process controls the buildability performance of 3D-printed structures. Therefore, numerical simulations were performed to observe their effect on the buildability of CDW-based geopolymer structures. Two significant process parameters were varied, i.e., printing speed (20 mm/s, 50 mm/s, and 80 mm/s) and layer width (15 mm, 30 mm, and 45 mm), and nine numerical simulations were performed. The voxel size and layer height were consistent per 3DCP experiments (15 mm). The numerical simulation file was prepared to complete the job in 40 steps, and the buildability of the structures was calculated based on the completed steps before structure failure during the simulation. The buildability predictions from numerical simulations are reported in Table 4 and graphically presented in Figure 2. The numerical modelling revealed an improved buildability with the increase in layer width of the 3D-printed mortar. For instance, at a printing speed of 20 mm/s, the buildability of the structures was 145.4 mm, 208.2 mm, and 410.6 mm at layer widths of 15 mm, 30 mm, and 45 mm, respectively.

Figure 2. Effect of printing speed and layer width on buildability of 3DCP structures.

Table 4. Sensitivity analysis on effect of process parameters on buildability of structures.

Run	Printing speed (mm/s)	Layer width (mm)	No. of steps to failure	Buildability (mm)
1	20	15	9.69	145.4
2	20	30	13.88	208.2
3	20	45	27.37	410.6
4	50	15	8.94	134.1
5	50	30	11.84	177.6
6	50	45	26.00	390.0
7	80	15	8.92	133.8
8	80	30	11.45	171.8
9	80	45	25.33	380.0

A similar trend was observed in other printing speeds, i.e., 50 mm/s and 80 mm/s, where an improved buildability was observed for increased layer width. For the structures 3D-printed at 50 mm/s, the buildability of 134.1 mm, 177.6 mm, and 390 mm was achieved for layer widths of 15 mm, 30 mm, and 45 mm, respectively. Finally, a buildability of 133.8 mm, 171.8 mm, and 380 mm was observed for structures fabricated using layer widths of 15 mm, 30 mm, and 45 mm, respectively, at a printing speed of 80 mm/s.

Furthermore, a decreased buildability was predicted for lower printing speed at the same layer width. For instance, a buildability of 145.4 mm, 134.1 mm, and 133.8 mm was achieved for structured 3D-printed at printing speeds of 20 mm/s, 50 mm/s, and 80 mm/s at a layer height of 15 mm. Likewise, the structures 3D-printed at printing speeds of 20 mm/s, 50 mm/s, and 80 mm/s revealed buildability of 208.2 mm, 177.6 mm, and 171.8 mm using 30 mm of layer width. Finally, a buildability of 410.6 mm, 390 mm, and 380 mm was recorded for structures 3D-printed at 20 mm/s, 50 mm/s, and 80 mm/s for the same layer width of 45 mm. Although the buildability did not vary much with the change in printing speed, lower printing speeds are recommended. From sensitivity analysis on the effect of process parameters, it is concluded that layer width is the most significant parameter; as the layer width increases, the 3D-printed layers can better withstand the upcoming material, resulting in improved buildability of the structures. The failure behaviour of all the simulation runs performed is presented in Figure 3.

Figure 3. Failure of 3DCP structures 3D-printed under different processing conditions.

4. Conclusions

In this study, a novel CDW-based mortar mixture was prepared for the 3DCP process. A square cross-sectional structure was designed and 3D-printed using a lab-scale gantry-type 3D printer for buildability analysis of the prepared geopolymer material. The geopolymer material was also characterized for time-dependent properties for use in a numerical model capable of predicting the buildability of concrete structures. In the numerical modelling and simulation phase, predictive simulations were performed for experimentally 3D-printed structures to validate the predictability of the numerical model. Finally, numerical simulations were performed to evaluate the impact of 3DCP process parameters (i.e., printing speed and layer width) on the buildability of structures. From 3DCP experiments and numerical modelling results, it is concluded that the novel CDW-based geopolymer mixture was found suitable for 3DP applications. An overall buildability of 200 mm was achieved for a squared profile structure. The numerical modelling results on the predictive performance of the 3DCP process were also satisfactory, with an error of 6.3%. Sensitivity analysis on the effect of designed height, printing speed, and layer width concluded that the designed height was not significant on the buildability performance of 3D-printed structures.

In addition, the buildability of the structures improved with the increase in layer width, and lower printing speeds were found to be optimum. However, layer width was observed to be the most significant parameter for 3DCP performance. Using waste materials and numerical prediction tools in the construction industry can help promote sustainability. Furthermore, the presented work can serve as a guideline to explore and adopt numerical modelling and simulation approaches for other novel geopolymer-based materials. Indeed, the experimental approach cannot be eliminated; however, the predictive performance of novel construction materials in the 3DP process can significantly reduce waste and resource utilization.

Acknowledgments

Open Access funding is provided by the College of Science and Engineering, Hamad Bin Khalifa University, Qatar. The authors gratefully acknowledge the financial assistance of the Qatar National Research Fund (QNRF; AICC02-0429-190014) and the Scientific and Technical Research Council of Turkey (TÜBITAK) provided under the TÜBITAK–QNRF Joint Funding Program (Project #119N030). The authors also gratefully acknowledge the financial assistance of TÜBITAK provided under Project #122M556. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of TÜBITAK or QNRF.

Disclosure statement

The authors declare no conflict of interest.