نتائج البحث

A Smart, Data-Driven Approach to Qualify Additively Manufactured Steel Samples for Print-Parameter-Based Imperfections.

• Authors : Alaparthi, Suresh^1,2 (AUTHOR); Subadra, Sharath P.^1,2 (AUTHOR) ; Sheikhi, Shahram^1,2 (AUTHOR)

• Source: Materials (1996-1944). Jun2024, Vol. 17 Issue 11, p2513. 20p.

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Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

• Authors : Jagadesh, P.¹ (AUTHOR) ; Khan, Afzal Hussain² (AUTHOR) ; Priya, B. Shanmuga¹ (AUTHOR)

Subjects: *SELF-consolidating concrete; *ARTIFICIAL neural networks; *MACHINE learning

• Source: PLoS ONE. 5/13/2024, Vol. 19 Issue 5, p1-39. 39p.

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Machine Learning Models for Mechanical and Micro Structural Properties of Recycled Fine Aggregate Concrete Using Different Mixing Approaches.

• Authors : Ranjith, A.¹ ; Yashwanth, M. K.²; Kiran, B. M.³

Subjects: *MACHINE learning; *CONSTRUCTION industry; *NATURAL resources

• Source: International Journal of Engineering Transactions B: Applications. May2024, Vol. 37 Issue 5, p997-1011. 15p.

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Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications.

• Authors : Guo, Xinghuang¹ (AUTHOR); Garcia, Cesar² (AUTHOR); Andrade Valle, Alexis Ivan^3,4 (AUTHOR)

Subjects: *COMPRESSIVE strength; *MACHINE learning; *ENVIRONMENTAL geotechnology

• Source: PLoS ONE. 4/2/2024, Vol. 19 Issue 4, p1-33. 33p.

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Comprehensive study on the Python-based regression machine learning models for prediction of uniaxial compressive strength using multiple parameters in Charnockite rocks.

• Authors : Kochukrishnan, Sowmya¹ (AUTHOR) ; Krishnamurthy, Premalatha¹ (AUTHOR); D., Yuvarajan² (AUTHOR)

• Source: Scientific Reports. 3/28/2024, Vol. 14, p1-12. 12p.

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Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm.

• Authors : Ho, Lanh Si^1,2 (AUTHOR); Tran, Van Quan¹ (AUTHOR)

Subjects: *CONCRETE masonry; *BOOSTING algorithms; *ARTIFICIAL neural networks

• Source: PLoS ONE. 3/5/2024, Vol. 19 Issue 3, p1-23. 23p.

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LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK-BASED WEAR PERFORMANCE PREDICTION OF AL 7050 ALLOY REINFORCED BY NANOPARTICLES OF WOLFRAM CARBIDE.

• Authors : KUMAR SAHU, S.¹ ; PRIYADHARSHINI, V. J.²; KOLA, R.³

• Source: Journal of the Balkan Tribological Association. 2024, Vol. 30 Issue 2, p192-207. 16p.

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基于金枪鱼群算法优化极限学习机的混凝土抗压强度预测.

• Authors : 张博吾¹ ; 耿秀丽¹

• Source: Application Research of Computers / Jisuanji Yingyong Yanjiu. Feb2024, Vol. 41 Issue 2, p444-449. 6p.

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Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil.

• Authors : Abdullah, Gamil M. S.¹ (AUTHOR); Ahmad, Mahmood^2,3 (AUTHOR) ; Babur, Muhammad⁴ (AUTHOR)

• Source: Scientific Reports. 1/28/2024, Vol. 14 Issue 1, p1-15. 15p.

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Estimation and Prediction of the Polymers' Physical Characteristics Using the Machine Learning Models.

• Authors : Malashin, Ivan Pavlovich¹ (AUTHOR) ; Tynchenko, Vadim Sergeevich^1,2,3 (AUTHOR) ; Nelyub, Vladimir Aleksandrovich¹ (AUTHOR)

Subjects: *MACHINE learning; *RANDOM forest algorithms; *MATERIALS science

• Source: Polymers (20734360). Jan2024, Vol. 16 Issue 1, p115. 27p.

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