Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

Izadgoshasb, Hamed; Kandiri, Amirreza; Shakor, Pshtiwan; Laghi, Vittoria; Gasparini, Giada

doi:10.3390/app112210826

Cited by 31 publications

(16 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adding recycled wastes, waste materials, and geo-polymers significantly impacts sustainability. Izadgoshasb et al (2021) developed a model combining a multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN), ANNMOGOA, to predict the compressive strength of 3DP concrete. Mix proportions and fresh-state properties of concrete are used as input for the ANNMOGOA.…”

Section: Designmentioning

confidence: 99%

A review of concrete 3D printed structural members

Raphael

Senthilnathan

Patel

et al. 2023

Front. Built Environ.

View full text Add to dashboard Cite

Concrete 3D Printing (3DP) is a potential technology for increasing automation and introducing digital fabrication in the construction industry. Concrete 3D Printing provides a significant advantage over conventional or precast methods, such as the prospects of topologically optimized designs and integrating functional components within the structural volume of the building components. Many previous studies have compiled state-of-art studies in design parameters, mix properties, robotic technologies, and reinforcement strategies in 3D printed elements. However, there is no literature review on using concrete 3D Printing technology to fabricate structural load-carrying elements and systems. As concrete 3DP is shifting towards a large-scale construction technology paradigm, it is essential to understand the current studies on structural members and focus on future studies to improve further. A systematic literature review process is adopted in this study, where relevant publications are searched and analyzed to answer a set of well-defined research questions. The review is structured by categorizing the publications based on issues/problems associated with structural members and the recent technology solutions developed. It gives an overall view of the studies, which is still in its nascent stage, and the areas which require future focus on 3D printing technology in large-scale construction projects.

show abstract

Section: Designmentioning

confidence: 99%

A review of concrete 3D printed structural members

Raphael

Senthilnathan

Patel

et al. 2023

Front. Built Environ.

View full text Add to dashboard Cite

show abstract

“…The G3DP specimens should gain the required compressive strength for construction applications besides satisfying the printability essentials (Bhattacherjee et al, 2021;Izadgoshasb et al, 2021). In an investigation, due to enhanced gel formation, the strength of G3DP and mold-cast samples continuously increased with increasing content of GGBS (Panda et al, 2019c;Chougan et al, 2020).…”

Section: Shape Retention Abilitymentioning

confidence: 99%

Geopolymer 3D printing: a comprehensive review on rheological and structural performance assessment, printing process parameters, and microstructure

Barve,

Bahrami,

Shah

2023

Front. Mater.

View full text Add to dashboard Cite

Geopolymers are under scrutiny as a sustainable alternative to cement in 3D printing for eco-friendly construction. Geopolymer 3D printing (G3DP) holds promise for green construction and advanced manufacturing. This study addresses G3DP’s rheological properties, printability, and microstructure analysis. Results indicate the pivotal role of the rheological properties in the printability, encompassing parameters like the pumpability, extrudability, and shape retention. Lower viscosity and appropriate yield stress are crucial. The structural performance of G3DP, given its inherent anisotropic nature and assessment techniques, is scrutinized. Process variables such as nozzle design and print speed and interval affect the printability, buildability, and structural properties. Research on the parameters’ optimization is necessary. Additionally, evaluation techniques for the G3DP’s rheological and structural behaviors require standardization. Understanding the G3DP’s rheology is paramount for the successful 3D printing construction. Findings offer quantitative insights into the importance of the rheological properties for the printability and structural performance. The microstructural analysis uncovers the porosity and density disparities compared to traditional geopolymers. This comprehensive review provides valuable insights for researchers and practitioners to enhance the G3DP’s application as a futuristic sustainable construction material.

show abstract

“…Machine learning empowers computers to learn from large data sets through model construction and training, making it an excellent tool for predicting material performance. − The current machine-learning methods for materials sciences are mainly based on experimental and simulated data to establish machine-learning models for predicting material performance. − These models include regression models, classification models, and deep learning models. , However, these machine learning methods commonly suffer from overfitting issues, leading to decreased predictive performance . Additionally, different materials and performances may require different machine-learning models, resulting in insufficient model generality and scalability.…”

Section: Introductionmentioning

confidence: 99%

Accurately Predicting Multiple Performance of 3D Printing Photopolymers Using Ensemble Learning

Gao,

Wang,

et al. 2024

ACS Appl. Polym. Mater.

View full text Add to dashboard Cite

The scarcity of high-performance 3D printing materials, particularly polymers, is the primary constraint on the high-end manufacturing capabilities of 3D printing. In this regard, accurately predicting the performance of 3D printing materials plays a crucial role in discovering high-performance 3D printing materials. However, conventional machine learning approaches, which depend on high-quality data, often face difficulties in achieving accurate and adaptable predictions, resulting in the inability to predict multiple material performances simultaneously. To overcome these limitations, we established a high-quality performance database for 3D printing photopolymers by carrying 110 sets of 3D printing experiments. Based on the database, an ensemble learning model capable of accurately predicting multiple performances for 3D printing materials was developed by integrating various machine learning models. The ensemble learning model designed successfully achieved high-precision predictions for the hardness, toughness, and strength of 3D printing photopolymers simultaneously. The determination coefficient (R 2 ) values for these predicted properties were 0.911, 0.905, and 0.954, successfully leveraging the powerful data processing capabilities of machine learning to establish an efficient mechanism for discovering high-performance 3D printing materials. This advancement would hold great potential for expanding the application of 3D printing and providing effective methods for developing materials in various fields.

show abstract

Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

Cited by 31 publications

References 44 publications

A review of concrete 3D printed structural members

A review of concrete 3D printed structural members

Geopolymer 3D printing: a comprehensive review on rheological and structural performance assessment, printing process parameters, and microstructure

Accurately Predicting Multiple Performance of 3D Printing Photopolymers Using Ensemble Learning

Contact Info

Product

Resources

About