Search Results - "Engineering structures"

Aluminium alloys as structural material: A review of research by Georgantzia, Evangelia, Gkantou, Michaela, Kamaris, George S.

“…•Research studies on the performance of structural aluminium alloys are discussed.•Research on material and structural members are tabulated.•Applicability of…”
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Emerging artificial intelligence methods in structural engineering by Salehi, Hadi, Burgueño, Rigoberto

“…•A review of recent applications of emerging artificial intelligence (AI) methods is presented.•The methods of pattern recognition, machine learning, and deep…”
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Metal 3D printing in construction: A review of methods, research, applications, opportunities and challenges by Buchanan, C., Gardner, L.

“…•Construction projects using 3D printing outlined and lessons learnt highlighted.•Review of metal 3D printing methods and their suitability for use in…”
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A systematic review of convolutional neural network-based structural condition assessment techniques by Sony, Sandeep, Dunphy, Kyle, Sadhu, Ayan, Capretz, Miriam

“…•A systematic review of CNN-based SHM literature is presented in this paper.•The review is categorized into multiple classes depending on the specific SHM.•The…”
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Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach by Mangalathu, Sujith, Hwang, Seong-Hoon, Jeon, Jong-Su

“…•Use of Shapely additive explanations for failure modes of RC columns and shear walls.•Importance factor for failure modes of RC columns and shear…”
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Research and practice on progressive collapse and robustness of building structures in the 21st century by Adam, Jose M., Parisi, Fulvio, Sagaseta, Juan, Lu, Xinzheng

“…[Display omitted] •Extreme events often cause local damage to building structures.•A column failure can lead to the progressive collapse of the entire…”
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Functionally graded graphene reinforced composite structures: A review by Zhao, Shaoyu, Zhao, Zhan, Yang, Zhicheng, Ke, LiaoLiang, Kitipornchai, Sritawat, Yang, Jie

“…•The successful fabrication of FG-GPLRC is reported.•Micromechanics models to predict effective mechanical properties of GPLRC are reviewed.•A comprehensive…”
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Novel visual crack width measurement based on backbone double-scale features for improved detection automation by Tang, Yunchao, Huang, Zhaofeng, Chen, Zheng, Chen, Mingyou, Zhou, Hao, Zhang, Hexin, Sun, Junbo

“…•Backbone neighborhood distribution points are reduced to facilitate classification.•Dual-scale backbone features are combined for accurate width measurement…”
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Sensors for process and structural health monitoring of aerospace composites: A review by Rocha, Helena, Semprimoschnig, Christopher, Nunes, João P.

“…•Review of sensors for embedment in composites for structural health monitoring.•Multi-purpose sensor embedment for process and structural health monitoring…”
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Inerter-based structural vibration control: A state-of-the-art review by Ma, Ruisheng, Bi, Kaiming, Hao, Hong

“…•Physical realizations and mechanical models of inerter are briefly presented.•Inerter-based vibration control systems and their applications are…”
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New advancements, challenges and opportunities of multi-storey modular buildings – A state-of-the-art review by Ferdous, Wahid, Bai, Yu, Ngo, Tuan Duc, Manalo, Allan, Mendis, Priyan

“…•Reviewed recent progress and directed future research for modular buildings.•Commonly used in UK, Sweden, Japan and USA with growing interest in Australia and…”
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Fiber-reinforced polymer composites in strengthening reinforced concrete structures: A critical review by Naser, M.Z., Hawileh, R.A., Abdalla, J.A.

“…•Review of Fiber-reinforced polymer (FRP) composites in infrastructure is presented.•Performance of FRP strengthening systems in extreme conditions is…”
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Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling by Zhang, Ruiyang, Liu, Yang, Sun, Hao

“…•A deep physics-guided convolutional neural network (PhyCNN) is developed for structural seismic response estimation.•Available physics can provide constraints…”
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Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review by Feng, Dongming, Feng, Maria Q.

“…•Summarized collective experience of vision-based dynamic response measurement and SHM.•Reviewed basics and principles of vision-based sensor system.•Discussed…”
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Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements by Feng, De-Cheng, Wang, Wen-Jie, Mangalathu, Sujith, Hu, Gang, Wu, Tao

“…•The ensemble machine learning is adopted to predict the shear strength of RC deep beams.•The model has a very high accuracy and also verified with…”
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A stress-path dependent stress-strain model for FRP-confined concrete by Lai, M.H., Liang, Y.W., Wang, Q., Ren, F.M., Chen, M.T., Ho, J.C.M.

“…•A stress-path dependent axial stress-strain model of FRP-confined concrete is proposed.•The model considers stress-path history of confinement by using an…”
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A path dependent stress-strain model for concrete-filled-steel-tube column by Lai, M.H., Song, W., Ou, X.L., Chen, M.T., Wang, Q., Ho, J.C.M.

“…•A stress-path dependent axial load-strain model of CFST column is proposed.•The effect of confining stress path dependence is studied.•The effects of…”
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Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach by Rahman, Jesika, Ahmed, Khondaker Sakil, Khan, Nafiz Imtiaz, Islam, Kamrul, Mangalathu, Sujith

“…•Data-driven 11 machine learning methods are employed to predict the shear capacity of SFRC beams.•10-fold cross-validations are performed on a data of 507…”
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Nonlinear dynamic behavior of simply-supported RC beams subjected to combined impact-blast loading by Zhang, Chunwei, Gholipour, Gholamreza, Mousavi, Asma Alsadat

“…•The nonlinear dynamic responses and failure behaviors of simply supported RC beams subjected to a combination of impact and blast loadings are…”
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Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM by Wakjira, Tadesse G., Ibrahim, Mohamed, Ebead, Usama, Alam, M. Shahria

“…•Develop machine learning-based flexural and load capacities model for RC beams strengthened in flexure with FRCM.•Explain the output of the proposed xgBoost…”
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