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

2021 ◽  
Vol 11 (22) ◽  
pp. 10826
Author(s):  
Hamed Izadgoshasb ◽  
Amirreza Kandiri ◽  
Pshtiwan Shakor ◽  
Vittoria Laghi ◽  
Giada Gasparini
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Cement Mortar ◽  
Iteration Process ◽  
Machine Learning Method ◽  
Structural Members ◽  
Grasshopper Optimization Algorithm ◽  
3D Printed ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann

Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.

scholarly journals Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 485
Author(s):  
Amirreza Kandiri ◽  
Farid Sartipi ◽  
Mahdi Kioumarsi
Keyword(s):  
Compressive Strength ◽  
Optimization Algorithms ◽  
Recycled Aggregate ◽  
Recycled Aggregates ◽  
Tree Model ◽  
Grasshopper Optimization Algorithm ◽  
Compressive Strength Of Concrete ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann ◽  
Industry Needs

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

scholarly journals Dynamic Properties and Fractal Characteristics of 3D Printed Cement Mortar in SHPB Test

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5554
Author(s):  
Yixin Mo ◽  
Songlin Yue ◽  
Qizhen Zhou ◽  
Bowei Feng ◽  
Xiao Liu
Keyword(s):  
Compressive Strength ◽  
Fractal Dimension ◽  
Energy Dissipation ◽  
3D Printing ◽  
Cement Mortar ◽  
Dynamic Properties ◽  
3D Printed ◽  
Fractal Characteristics ◽  
Dynamic Compressive Strength ◽  
Printing Direction

Comparing with the traditional construction process, 3D printing technology used in construction offers many advantages due to the elimination of formwork. Currently, 3D printing technology used in the construction field is widely studied, however, limited studies are available on the dynamic properties of 3D printed materials. In this study, the effects of sand to binder ratios and printing directions on the fractal characteristics, dynamic compressive strength, and energy dissipation density of 3D printed cement mortar (3DPCM) are explored. The experiment results indicate that the printing direction has a more significant influence on the fractal dimension compared with the sand to binder ratio (S/B). The increasing S/B first causes an increase and then results in a decline in the dynamic compressive strength and energy dissipation of different printing directions. The anisotropic coefficient of 3DPCM first is decreased by 20.67%, then is increased by 10.56% as the S/B increases from 0.8 to 1.4, showing that the anisotropy is first mitigated, then increased. For the same case of S/B, the dynamic compressive strength and energy dissipation are strongly dependent on the printing direction, which are the largest printing in the Y-direction and the smallest printing in the X-direction. Moreover, the fractal dimension has certain relationships with the dynamic compressive strength and energy dissipation density. When the fractal dimension changes from 2.0 to 2.4, it shows a quadratic relationship with the dynamic compressive strength and a logarithmic relationship with the energy dissipation density in different printing directions. Finally, the printing mortar with an S/B = 1.1 is proved to have the best dynamic properties, and is selected for the 3D printing of the designed field barrack model.

scholarly journals INVESTIGATION OF LIGHTWEIGHT STRUCTURAL MATERIALS PRODUCED USING ALUMINUM SCRAPS WITH CEMENT MORTAR

2021 ◽  
pp. 1-6
Author(s):  
ABDULQADER NIHAD NOORI
Keyword(s):  
Compressive Strength ◽  
Ordinary Portland Cement ◽  
Cement Mortar ◽  
Environmental Concerns ◽  
Structural Members ◽  
Replacement Ratio ◽  
Solid Aluminum ◽  
Aluminum Scrap ◽  
Commercial Activities ◽  
The World

A lot of environmental concerns are increasing day after day result in the raise of solid waste in large quantities in the world resulting from the demolition of buildings and various industrial and commercial activities. This research provides the possibility of reusing one of these wastes solid aluminum scrap (Als) by using it to produce a modified type of cement mortar. The research focuses on the mechanical behavior of the new cement mortar type obtained by adding aluminum scrap by different percentages (1%, 2%, 3%, 4%, and 5%) as a replacement ratio from the weight of sand mixed with Ordinary Portland Cement (OPC). The findings of this research indicated the possibility of using aluminum waste material in certain limits where the compressive strength significantly reduced by increasing the percentage of Als. The most interesting observation was to increase the volume of the mixture by increasing the ratio of Als. According to the results, it is possible to use this type of cement mortar to produce lightweight structural members such as slabs, bricks, etc. Finally, the general formulation was proposed based on the regression analysis and experimental measurements to give a capture of the compressive strength of mortar under any variables alter (age of specimen and/or quantity of aluminum replacement).

scholarly journals Online Healthcare Medium for Disease-Treatment using Modified ANN based Classification and Ranking

2020 ◽  
Vol 9 (3) ◽  
pp. 4029-4036
Keyword(s):  
Neural Network ◽  
Information Technologies ◽  
Online Review ◽  
Current Investigation ◽  
Weight Optimization ◽  
Disease Treatment ◽  
User Query ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann

The fundamental purpose of the healthcare information medium in social networks is centered on ascertaining the opinions of several people regarding specific user queries. In the backdrop of ever-increasing accessibility and attractiveness of the opinion-rich resources as evidenced by the online review sites and personal blogs, the emerging opportunities and challenges dynamically make use information technologies to go in for and to comprehend the outlook of the vast majority of users. However, it is unfortunate that the time-honored finds its waterloo in locating the impending issue of deploying internet with a view to identify and generate appropriate conclusions regarding the specified ailments. The current investigation effectively carries out the function of processing the user query with the able assistance of the MedHelp website and subsequently forwards the pertinent traits to the sentiwordnet for performing the sentimental examination. It is followed by the creation of the score in accordance with the positivity and negativity of the content in the website. In this regard, the Artificial Neural Network (ANN) is ably guided with the aim of creating rank for the websites. And the weight optimization for ANN is elegantly executed by the efficient Grasshopper Optimization Algorithm (GOA). The technique is performed on the powerful platform of JAVA and the consequent outcomes assessed exhibits incredible decrease in the error rate.

Incorporation of Iraqi Rocks in the Production of Eco-Friendly Cement Mortar

2020 ◽  
Vol 38 (10A) ◽  
pp. 1522-1530
Author(s):  
Rawnaq S. Mahdi ◽  
Aseel B. AL-Zubidi ◽  
Hassan N. Hashim
Keyword(s):  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Compressive Strength ◽  
Water Absorption ◽  
Structural Properties ◽  
Mechanical Milling ◽  
Cement Mortar ◽  
Cement Mortars

This work reports on the incorporation of Flint and Kaolin rocks powders in the cement mortar in an attempt to improve its mechanical properties and produce an eco-friendly mortar. Flint and Kaolin powders are prepared by dry mechanical milling. The two powders are added separately to the mortars substituting cement partially. The two powders are found to improve the mechanical properties of the mortars. Hardness and compressive strength are found to increase with the increase of powders constituents in the cement mortars. In addition, the two powders affect water absorption and thermal conductivity of the mortar specimens which are desirable for construction applications. Kaolin is found to have a greater effect on the mechanical properties, water absorption, and thermal conductivity of the mortars than Flint. This behavior is discussed and analyzed based on the compositional and structural properties of the rocks powders.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction

scholarly journals Mechanical Behavior of Brick Masonry in an Acidic Atmospheric Environment

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2694 ◽  
Author(s):  
Shansuo Zheng ◽  
Lihua Niu ◽  
Pei Pei ◽  
Jinqi Dong
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Fly Ash ◽  
Acid Rain ◽  
Cement Mortar ◽  
Brick Masonry ◽  
Atmospheric Environment ◽  
Peak Strain ◽  
Lime Mortar ◽  
Attenuation Model

In order to evaluate the deterioration regularity for the mechanical properties of brick masonry due to acid rain corrosion, a series of mechanical property tests for mortars, bricks, shear prisms, and compressive prisms after acid rain corrosion were conducted. The apparent morphology and the compressive strength of the masonry materials (cement mortar, cement-lime mortar, cement-fly ash mortar, and brick), the shear behavior of the masonry, and the compression behavior of the masonry were analyzed. The resistance of acid rain corrosion for the cement-lime mortar prisms was the worst, and the incorporation of fly ash into the cement mortar did not improve the acid rain corrosion resistance. The effect of the acid rain corrosion damage on the mechanical properties for the brick was significant. With an increasing number of acid rain corrosion cycles, the compressive strength of the mortar prisms, and the shear and compressive strengths of the brick masonry first increased and then decreased. The peak stress first increased and then decreased whereas the peak strain gradually increased. The slope of the stress-strain curve for the compression prisms gradually decreased. Furthermore, a mathematical degradation model for the compressive strength of the masonry material (cement mortar, cement-lime mortar, cement-fly ash mortar, and brick), as well as the shear strength attenuation model and the compressive strength attenuation model of brick masonry after acid rain corrosion were proposed.

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