Application of Machine Learning in Prediction of Shear Capacity of Headed Steel Studs in Steel–Concrete Composite Structures

Avci-Karatas, Cigdem

doi:10.1007/s13296-022-00589-z

Cited by 13 publications

(7 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, Degtyarev et al [58] expanded research on two typical cases (NSC and light-strength concrete (LSC)), with a reliability evaluation, according to design codes using nine ML regression models. Avci-Karatas et al [59] highlighted the use of advanced ML techniques, such as minimax probability machine regression and extreme ML, in order to enhance the accuracy and precision of ML models. Zhu et al [60] developed an ML model that combines an ANN model with several advanced hyperparameter optimizations, such as an ANN-particle swarm optimization (PSO) and an ANN-improved eliminate particle swamp optimizer (IEPSO).…”

Section: Literature Reviewmentioning

confidence: 99%

Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections

Roh,

Vu,

Jeon

et al. 2024

Buildings

View full text Add to dashboard Cite

Existing design codes for predicting the strength of stud shear connections in composite structures are limited when adapting to constant changes in materials and configurations. Machine learning (ML) models for predicting shear connection are often constrained by the number of input variables, resembling conventional design equations. Moreover, these models tend to overlook considerations beyond those directly comprising the connection. In addition, the data used in ML are often biased and limited in quantity. This study proposes a model using AutoML to automate and optimize the process for predicting the ultimate strength and deformation capacity of shear connections. The proposed model leverages a comprehensive dataset derived from experimental studies and finite element analyses, offering an advanced data-driven solution to overcome the limitations of traditional empirical equations. A digital twin model for the static design of pushout specimens was defined to replace existing empirical design codes. The digital twin model incorporates predictions of the geometry model, ultimate strength, and slip as input parameters and provides criteria for evaluating the limit state through a bilinear load–slip curve. This study advances predictive methodologies in structural engineering by emphasizing the importance of ML in addressing the dynamic and multifaceted nature of shear connection behaviors.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections

Roh,

Vu,

Jeon

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Teniendo en cuenta algunas configuraciones típicas en casos de estudio similares que recomiendan estructuras en acero para sistemas de soporte de paneles solares, se encontraron parámetros que las diferencian a nivel de arriostramientos laterales, tipo de conexión entre elementos estructurales y condiciones de apoyo [13,17,18]. Así, para los marcos de soporte a estudiar en el caso del relleno sanitario "el carrasco" se definieron tres configuraciones, donde la primera se idealizó arriostrada en una sola dirección con apoyos empotrados, la segunda arriostrada lateralmente en dos direcciones con apoyos articulados y la tercera con conexiones rígidas, sin arriostramiento lateral y con apoyos empotrados.…”

Section: Análisis De Alternativas Para Estructuras De Soporteunclassified

Análisis de viabilidad estructural de un sistema de generación de energía solar fotovoltaica en un relleno sanitario

Gutiérrez Vega,

Mendoza Rojas,

Cotes Prieto

et al. 2023

Investigación e Innovación en Ingenierías

View full text Add to dashboard Cite

Objetivo: Análisis de viabilidad estructural para configuraciones de soporte de paneles solares en rellenos sanitarios clausurados para un mejor aprovechamiento de estos espacios contribuyendo a aumentar la capacidad del país de generación de energía renovable en espacios donde la afectación de ecosistemas es baja o nula. Metodología: Con base en los parámetros de análisis identificados como relevantes, se ejecutó una revisión bibliográfica que permitió identificar seis tipologías estructurales y con base en modelado numérico realizar un análisis para los componentes de seguridad estructural, afectaciones por asentamientos diferenciales y costos. Resultados: Se analizaron las diferentes alternativas de estructuras de soporte con base en los parámetros planteados en la metodología. Adicionalmente en el análisis de cargas se logró identificar que la carga crítica de diseño corresponde a la carga de viento. Con base en un análisis comparativo se recomendó el uso de la normativa japonesa JIS C 8955 para el diseño de este tipo de estructuras de soporte. Conclusiones: Se identifica la opción de estructura de soporte con elementos de arriostramiento lateral, apoyos articulados y zapatas aisladas como la más recomendada para este tipo de implementaciones.

show abstract

“…ML models and Gene Expression Programming (GEP) have seen extensive application in civil engineering, notably in predicting the shear resistance of steel structures. For example, Avci-Karatas [16] used minimax probability machine regression (MPMR) and extreme machine learning (EML) to predict the shear capacity of headed studs. The models were developed using experimental data and key input parameters such as stud shank diameter, and tensile strength of headed steel studs.…”

Section: Introductionmentioning

confidence: 99%

Five Machine Learning Models Predicting the Global Shear Capacity of Composite Cellular Beams with Hollow-Core Units

Ferreira,

Jeong,

Mansouri

et al. 2024

Preprint

View full text Add to dashboard Cite

The calculation of the global shear capacity of steel-concrete composite downstand cellular beams with precast hollow-core units is important as it affects the span to depth ratios and the amount of material used, hence affects the embodied CO2 calculation when designers are deciding on the floor grids. This paper presents a reliable tool that can be used by designers to alter and optimise grip options during the preliminary design stages, without the need to run onerous calculations. The global shear capacity prediction formula is developed using five machine learning models. First, a finite element model database is developed. The influence of the opening diameter, web opening spacing, tee-section height, concrete topping thickness, the interaction degree, and the number of shear studs above the web opening are investigated. Reliability analysis is conducted to assess the design method and propose new partial safety factors. The Catboost Regressor algorithm presented better accuracy compared to the other algorithms. An equation to predict the shear capacity of composite cellular beams with hollow-core units is proposed by Gene Expression Programming. In general, the partial safety factor for resistance, according to the reliability analysis, varied between 1.25 and 1.26.

show abstract

Application of Machine Learning in Prediction of Shear Capacity of Headed Steel Studs in Steel–Concrete Composite Structures

Cited by 13 publications

References 52 publications

Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections

Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections

Análisis de viabilidad estructural de un sistema de generación de energía solar fotovoltaica en un relleno sanitario

Five Machine Learning Models Predicting the Global Shear Capacity of Composite Cellular Beams with Hollow-Core Units

Contact Info

Product

Resources

About