# Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
## 1 Introduction  
Extrusion 3 DP is known as one of the most promising techniques for the con-struction industry. Therefore, it is also referred to as \"number one 3 DP technology in Additive Manufacturing (AM) for construction\" [1, 2]. AM is a digital manufacturing technique to produce 3D printed parts and works based on a layer by layer printing which is supported by CAD files. This technique could help to construct complicated geometries which are hard to fabricate by formwork. In recent years, AM techniques endeavour to produce great structural members which can be suitable to build on Earth and beyond planet Earth.  
Although there are many studies on predicting concrete mechanical strength for conventional concrete casting, there is not any convenient study or only a few on ma-chine learning (ML) for 3D printing concrete. For example, [PERSON], et al. [3] used 1200 dataset samples for the evaluation of the concrete compressive strength of mix designs with coarse aggregates smaller than 20 mm. In their study, they have developed three models and they have selected the best suited model which can be applicable to use in predicting the compressive strength. According to their investigation, the cement con-tent in the concrete matrix has a major influence on the concrete compressive strength.  
ML is another tool of digital transformation in the 21 st century ML allows the computer to learn automatically from the entry data and make the decision and pre-dictions without having been specifically programmed [4].  
Having ML technology with a large number of collected data would be beneficial to use as an Artificial Intelligence (AI) to prove the quality of production and predictingmechanical strength. [5]. It is vital to address that the data alone cannot provide enough information without having it analysed and interpreted correctly [6].  
According to an earlier study by [PERSON] and [PERSON] [6], AI includes three main AM applications, namely Powder-Bed Fusion, Directed Energy Deposition and Material Extrusion. However, it can be stated that all types of AM could be part of AI as long as sufficient information and data on them is available [7].  
According to the study of [PERSON], et al. [8], all types of neural networks refer to ar-tificial neural networks (ANN). Usually, a neural network (NN) comprises an input layer which is usually one or more hidden layers and one or more output layers. The layer is created of numerous neurons and the data of each neuron is spread to the next layer based on different weights. In ANN, categorized under regular NN, the spreading neurons form as a cycle and are fed through NN. Moreover, while it is under training condition, each neuron's weight would be optimized by the learning instruction and new information is imported into the NN. The most popular learning rule for NN is the Back-Propagation (BP) algorithm [9; 10] which controls weight based on the gradient descent.  
[PERSON], et al. [11] used NN in the AM process to predict the tensile strength for the printed part, the RMSE value recorded only 2%. Figure 1 describes the NN which was performed made during training when the output of each input is spreading backward in the NN to adjust the relevance of each input feature. After training, a new combina-tion of input features is spreading forward to guess the tensile strength value. Generally, NN displayed excellent performance in regression tasks, but it still requires to have some essential tuning of some hyperparameters, such as the number of hidden neurons and layers. Therefore, in this paper, a multi-objective optimization algorithm is used to find the optimal structure of the ANN for predicting compressive strength of 3 DP concrete.