Machine learning applications for developing sustainable construction materials

Adel, Hossein; Ghazaan, Majid Ilchi; Korayem, Asghar Habibnejad

doi:10.1016/b978-0-323-90508-4.00002-2

Cited by 9 publications

(4 citation statements)

References 81 publications

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“…Produksi semen merupakan salah satu sumber utama emisi karbon dioksida (CO2) yang tentunya menimbulkan ancaman lingkungan yang besar. Produksi semen berkontribusi sebesar 5-7% dari emisi CO2 di seluruh dunia [3,12], mengingat semen Portland dikenal luas sebagai bahan pengikat yang paling banyak digunakan dalam beton. Tren peningkatan penggunaan beton telah menyebabkan volume produksi semen Portland meningkat setiap tahunnya [13], sehingga diperkirakan permintaan produksi semen akan melampaui 3,7-4,4 miliar ton dalam tiga puluh tahun ke depan [3,12], terlebih lagi dengan tren penggunaan beton mutu tinggi memadat mandiri [11].…”

Section: Pendahuluanunclassified

Penetrasi dan Sorptivitas Klorida Beton Mutu Tinggi Memadat Mandiri dengan Variasi Substitusi Metakaolin

Deni,

Alfianarrochmah,

Kurnianingsih

2023

J-Sil

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Struktur beton bertulang seringkali terekspos pada lingkungan yang mengandung zat-zat perusak seperti ion klorida. Penetrasi ion klorida akan merusak lapisan pasif pada beton dan mengakibatkan terjadinya korosi, sehingga penggunaan beton berkualitas baik sangat dibutuhkan. Pada penelitian ini, beton mutu tinggi memadat mandiri dengan variasi substitusi metakaolin terhadap berat semen sebesar 0% (kontrol); 10%; 12,5%; 15%; 17,5 %; dan 20% digunakan sebagai solusi. Tujuan penelitian adalah mengidentifikasi pengaruh substitusi metakaolin dan kadar optimumnya untuk meningkatkan resistensi beton terhadap penetrasi klorida. Penelitian ini menggunakan metode eksperimental dengan benda uji silinder beton berdiameter 7,5 cm dan tinggi 15 cm. Pengujian kedalaman penetrasi klorida menggunakan metode uji penetrasi klorida laboratorium dipercepat, benda uji dipaparkan pada lingkungan klorida dengan konsentrasi 2% dengan variasi waktu pemaparan 15, 37, dan 51 hari dan kemudian dianalisis menggunakan metode AgNO3 colorimetric (Fu et al., 2022). Pengujian sorptivitas klorida dilakukan berdasarkan ASTM C.1585 (ASTM International, 2013) menggunakan variasi waktu 30, 90, dan 1440 menit. Dari hasil penelitian, didapat bahwa substitusi metakaolin 15% merupakan kadar optimum untuk menurunkan penetrasi dan sorptivitas klorida pada beton sebagai kontribusi dari reaksi pozzolaniknya. Penelitian diharapkan dapat bermanfaat sebagai pencegahan kerusakan beton bertulang pada lingkungan agresif dengan menghasilkan analisis beton bermutu baik dan ramah lingkungan. Reinforced concrete structures are often exposed to environments containing corrodents such as chloride ions. The penetration of chloride ions will deteriorate the passive layer of the concrete and result in corrosion, thus the use of good-quality concrete is required. In this study, high-strength self-compacting concrete with a variation of metakaolin substitution to cement weight of 0% (control); 10%; 12.5%; 15%; 17.5%; and 20% was used as a solution. The objective of the study was to identify the effect of metakaolin substitution and its optimum level to improve concrete resistance to chloride penetration. This research used an experimental method with concrete cylindrical specimens of 7.5 cm diameter and 15 cm height. The chloride penetration depth test used the accelerated laboratory chloride penetration test method, the specimens were exposed to a chloride environment with a concentration of 2% with a variation of exposure times of 15, 37, and 51 days and then analyzed using the colorimetric AgNO3 method (Fu et al., 2022). The chloride sorptivity test was conducted based on ASTM C.1585 (ASTM International, 2013) using time variations of 30, 90, and 1440 minutes. Based on the results, it was found that 15% metakaolin substitution is the optimum level to reduce the penetration and sorptivity of chloride in concrete as a contribution to its pozzolanic reaction. The research is expected to be useful in the prevention of deterioration of reinforced concrete in aggressive environments by producing good quality and environmentally friendly concrete analysis.

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Section: Pendahuluanunclassified

Penetrasi dan Sorptivitas Klorida Beton Mutu Tinggi Memadat Mandiri dengan Variasi Substitusi Metakaolin

Deni,

Alfianarrochmah,

Kurnianingsih

2023

J-Sil

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“…The utilization of sustainable materials is a global demand in the construction industry. Adel et al [18] surveyed the application of machine learning, such as ANNs, evolutionary algorithms, and support vector machines, to develop construction materials with sustainability considerations. The properties of sustainable mortar with different mixtures of ingredients were estimated using the ANN technique by Naderpour and Mirrashid [19].…”

Section: Sustainable Construction Materialsmentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.

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“…As a result, construction sites experience delays, and the testing processes adds extra cost and effort to the project. Testing specimens for distinct ingredient combinations with variable quantities at various ages is neither practicable nor sensible because of being time-consuming, costly, and reliant on real investigations [26][27][28]. It is difficult to accurately predict the mechanical characteristics of cementitious composites through experiment and formula fitting because of the varied features of CNTs, such as length, content, dispersion, and diameter.…”

Section: Introductionmentioning

confidence: 99%

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Zhu,

Wu,

et al. 2024

Buildings

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The standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays. Because carbon nanotubes (CNTs) vary in length, composition, diameter, and dispersion, experiment and formula fitting alone cannot reliably predict the strength of CNTs-based composites. For empirical equations or traditional statistical approaches to properly forecast complex materials’ mechanical characteristics, various significant parameters, databases, and nonlinear relationships between variables must be considered. Machine learning (ML) tools are the most advanced for accurate predictions of material behaviour. This study employed gradient boosting, light gradient boosting machine, and extreme gradient boosting techniques to forecast the CS of CNTs-modified concrete. Also, in order to explore the influence and interaction of various features, an interaction analysis was conducted. In terms of R2, gradient boosting, light gradient boosting machine, and extreme gradient boosting models proved their accuracy. Extreme gradient boosting had the highest R2 of 0.97, followed by light gradient boosting machine and gradient boosting with scores of 0.94 and 0.93, respectively. This type of research may help both academics and industry forecast material properties and influential elements, thereby reducing lab test requirements.

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Machine learning applications for developing sustainable construction materials

Cited by 9 publications

References 81 publications

Penetrasi dan Sorptivitas Klorida Beton Mutu Tinggi Memadat Mandiri dengan Variasi Substitusi Metakaolin

Penetrasi dan Sorptivitas Klorida Beton Mutu Tinggi Memadat Mandiri dengan Variasi Substitusi Metakaolin

Artificial Neural Networks for Sustainable Development of the Construction Industry

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

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