Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms

Fakharian, Pouyan; Eidgahee, Danial Rezazadeh; Akbari, Mahdi; Jahangir, Hashem; Taeb, Amir Ali

doi:10.1016/j.istruc.2022.12.007

Cited by 30 publications

(17 citation statements)

References 43 publications

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“…Furthermore, new algorithms that incorporate greater parameters provide interesting answers to the issues of inefficient data processing in huge data sets. These advances are aided by the use of big data and AI technology [143,144]. The review of the past literature showed that AI is a promising way to analyze the huge datasets that come from monitoring the health of bridges, which is difficult and complicated when using with traditional methods.…”

Section: Discussion and Remarksmentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

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The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

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Section: Discussion and Remarksmentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

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“…Water, sand, cement, and foaming agents + aggregates [13] Lime, cement, sand, aluminum powder + energy stimulus [14] Sand, water, cement + mold [15] Sand, water, cement + mold [16] Manufacturing method; equipment Prefabrication or in situ; mortar mixer and foam generator [13] Prefabrication; high pressure autoclave [17] Prefabrication [15] Prefabrication or in situ [16] Compressive strength, MPa <51.18 [18] <12 [14] <40.5 [19] 20-50 [20] Density, kg/m 3  Grouting, thermal insulation: 300-600  Non-load bearing structures: 600-1200 Non-structural elements: 700 [14] Structural and nonstructural elements: 1700-2000 [21] Structural elements: 2400 [22] Lime, cement, sand, aluminum powder + energy stimulus [14] Sand, water, cement + mold [15] Sand, water, cement + mold [16] Manufacturing method; equipment Prefabrication or in situ; mortar mixer and foam generator [13] Prefabrication; high pressure autoclave [17] Prefabrication [15] Prefabrication or in situ [16] Compressive strength, MPa <51.18 [18] <12 [14] <40.5 [19] 20-50 [20] Density, kg/m 3…”

Section: Compositionmentioning

confidence: 99%

Fire Resistance of Foamed Concrete for Discontinuous Partition Filling

Sulik,

Kukfisz,

Dowbysz

et al. 2024

Materials

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Lightweight concrete exhibits many advantages over traditional concrete such as lower density and thermal conductivity and an easier, cheaper, less energy-consuming manufacturing process. In order to extend its applications, there is a need to study its behavior in fire situations. Due to that, the aim of this study was to assess the fire resistance of foam concrete, depending on its thickness and the foaming process applied. Fire resistance was assessed according to EN 1363-1. The results indicate the usefulness of foam concrete in terms of isolating fire temperatures for discontinuous partition filling that are consequently a real alternative to dedicated solutions in the field of passive fire protection. The density of foam concrete was shown to have a large effect on the ability to insulate fire temperatures with a standard material preparation process. It was also noted that changing the method to continuous foam feeding may result in the achievement of similar values while maintaining foam concrete low density.

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“…ML can be used as a tool to improve operational intelligence in various fields. ML approaches consider computational and statistical techniques to gain knowledge straight from data, rather than relying on a predetermined formula [21,22].…”

Section: Machine Learningmentioning

confidence: 99%

Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis

Haghshenas,

Astarita,

Guido

et al. 2023

JCCE

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Traffic flow analysis is an interesting study topic in transportation studies. A better understanding of traffic flow is essential for more effective traffic reduction methods. Because managing traffic flow in cities is getting more complicated, we need more methodical ways to deal with these problems. Machine learning techniques have been suggested as a possible solution because they can process great amounts of data and give insights that can be used to help make decisions about how to manage traffic. The main objective of this research is to conduct a comprehensive examination of the quantitative and qualitative aspects of utilizing machine learning techniques in the management of traffic flow. Using the Web of Science (WOS) platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using machine learning techniques more and more over the past few years. This study shows the different approaches and methods that were used, as well as the results and limits of these methods. The results recommend that machine learning can be a useful tool for managing traffic flow in cities, but further investigation is warranted to gain a complete comprehension of both the advantages and disadvantages of the subject under scrutiny.

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Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms

Cited by 30 publications

References 43 publications

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

Fire Resistance of Foamed Concrete for Discontinuous Partition Filling

Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis

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