4.7 Article

Machine learning approaches for lateral strength estimation in squat shear walls: A comparative study and practical implications

Related references

Note: Only part of the references are listed.
Article Engineering, Civil

Seismic performance of squat UHPC shear walls subjected to high-compression shear combined cyclic load

Yue-Yi Li et al.

Summary: This study conducted cyclic tests on UHPC squat shear walls with high design axial load ratio and varying web distributed reinforcement ratio, along with one high-strength concrete counterpart. The results comprehensively investigated the seismic performance of UHPC walls, including failure mode, crack pattern, force-displacement relationship, deformation mechanism, energy dissipation, strength and stiffness degradation. Design suggestions were also proposed based on the findings. The test results show that UHPC specimens have excellent crack control ability, improved shear capacity and ultimate deformation compared to high-strength concrete specimens.

ENGINEERING STRUCTURES (2023)

Article Computer Science, Artificial Intelligence

Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications

Khuong Le Nguyen et al.

Summary: This study presents a comprehensive and rigorous process for developing an appropriate machine learning model to predict the shear strength of RC deep beams. The process includes the development of ML models, selection of input features, optimization of the training process, assessment of data randomness, comparison to conventional practice codes, and development of a web-based design platform.

EXPERT SYSTEMS WITH APPLICATIONS (2023)

Article Construction & Building Technology

Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs

Khuong Le Nguyen et al.

Summary: This paper investigates the effectiveness of machine learning models in MATLAB apps for predicting punching shear strength in reinforced concrete slabs. The results show that these models outperform empirical models and offer a reliable tool for real-time predictions.

ADVANCES IN CIVIL ENGINEERING (2023)

Article Construction & Building Technology

Interpretable machine learning models for the estimation of seismic drifts in CLT buildings

Eknara Junda et al.

Summary: This paper presents and compares several machine learning models for estimating peak inter-storey and roof drifts in multi-storey cross-laminated timber (CLT) walled structures. The models are trained and tested using a large collection of acceleration records, and feature selection techniques are used to identify the most efficient features. The accuracy of the model predictions is verified, and the influence of key input features on the model outputs is analyzed using the SHapley Additive exPlanation method (SHAP). The estimated drifts are compared with previous proposals and design code assumptions, and the potential causes of disagreement are discussed.

JOURNAL OF BUILDING ENGINEERING (2023)

Article Computer Science, Interdisciplinary Applications

Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls

Behrooz Keshtegar et al.

Summary: A hybrid artificial intelligence model, combining artificial neural network with adaptive harmony search optimization algorithm, demonstrated superior performance in predicting the ultimate shear capacity of reinforced concrete shear walls. The soft-computing model was proven to be more accurate than existing empirical relations.

ENGINEERING WITH COMPUTERS (2022)

Article Construction & Building Technology

Experimental study on seismic behavior of reinforced concrete shear walls with low shear span ratio

Feng Wei et al.

Summary: The study investigated the seismic behavior of squat reinforced concrete shear walls with different shear span ratios (SSR). It found various failure modes under different SSR conditions and evaluated the conservatism and accuracy of shear strength formulas in different design codes.

JOURNAL OF BUILDING ENGINEERING (2022)

Article Construction & Building Technology

Prediction of failure modes, strength, and deformation capacity of RC shear walls through machine learning

Haoyou Zhang et al.

Summary: This study introduces a technique for predicting the seismic performance of reinforced concrete walls using machine learning methods. The XGBoost and GB algorithms accurately predicted the failure modes of RC walls, while the gradient boosting and random forest algorithms performed best in predicting the lateral strength and ultimate drift ratio of RC walls. The flexure-to-shear strength ratio and shear-to-span ratio of RC walls had a greater influence on the failure modes of RC walls.

JOURNAL OF BUILDING ENGINEERING (2022)

Article Engineering, Civil

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong Le-Nguyen et al.

Summary: This study conducted a reliability analysis of the strength model for predicting the confinement influence of concrete columns with Fabric-Reinforced Cementitious Matrix (FRCM) using both physical models and a Deep Neural Network model. The results showed that the proposed ANN models accurately predicted the compressive strength of FRCM with high accuracy. The unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio were found to be the most significant input variables in the efficiency of FRCM confinement prediction.

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2022)

Article Construction & Building Technology

Strength prediction of circular CFST columns through advanced machine learning methods

Chao Hou et al.

Summary: This study evaluated the feasibility of combining mechanism analysis with machine learning models for predicting the axial compression strength of circular CFST columns, establishing several ML models for strength prediction and finding that the GPR model had higher accuracy and wider applicability range. The performance of ML models varied slightly under different column slenderness conditions, with random subdivisions having little effect on the model accuracy.

JOURNAL OF BUILDING ENGINEERING (2022)

Article Construction & Building Technology

Buckling and ultimate load prediction models for perforated steel beams using machine learning algorithms

Vitaliy V. Degtyarev et al.

Summary: This paper presents seven machine learning models for predicting the elastic buckling and ultimate loads of steel cellular beams. The optimized models demonstrated excellent agreement with numerical data. Additionally, a user-friendly web application was created for predictions on any device.

JOURNAL OF BUILDING ENGINEERING (2022)

Article Computer Science, Interdisciplinary Applications

A practical ANN model for predicting the PSS of two-way reinforced concrete slabs

Viet-Linh Tran et al.

Summary: This study developed an artificial neural network model using 218 test results to accurately predict the punching shear strength of two-way reinforced concrete slabs. Comparative results showed that this model provided the most accurate prediction.

ENGINEERING WITH COMPUTERS (2021)

Article Computer Science, Artificial Intelligence

Predicting load capacity of shear walls using SVR-RSM model

Behrooz Keshtegar et al.

Summary: This paper presents a novel hybrid intelligent model to predict the ultimate shear capacity of reinforced concrete shear walls (RCSW), which shows superior accuracy and lower uncertainty compared to existing design codes and empirical models.

APPLIED SOFT COMPUTING (2021)

Review Construction & Building Technology

Machine learning applications for building structural design and performance assessment: State-of-the-art review

Han Sun et al.

Summary: This paper reviews the historical development and recent advances of machine learning in building structural design and performance assessment, including predicting structural response and performance, interpreting experimental data, information retrieval using images and text, and recognizing patterns in structural health monitoring data. The challenges of integrating machine learning into structural engineering practice are identified, along with discussions on future research opportunities.

JOURNAL OF BUILDING ENGINEERING (2021)

Article Construction & Building Technology

Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls

De-Cheng Feng et al.

Summary: In this study, an advanced machine learning model was trained and interpreted for estimating the shear strengths of squat RC walls, utilizing a database of 434 samples. The model strategically combined the XGBoost algorithm for predictive modeling and the SHAP algorithm for analyzing factor importance. This setup achieved a high level of accuracy in shear strength estimation and provided physical and quantitative interpretations of the input-output dependencies.

JOURNAL OF STRUCTURAL ENGINEERING (2021)

Article Engineering, Civil

A machine learning-based formulation for predicting shear capacity of squat flanged RC walls

Duy-Duan Nguyen et al.

Summary: This study developed an artificial neural network (ANN) model for predicting the shear strength of squat flanged reinforced concrete walls, which proved to predict the shear capacity more accurately than existing equations. It also proposed a predictive formula covering thirteen input parameters and established an efficient graphical user interface (GUI) platform for practical design processes.

STRUCTURES (2021)

Article Engineering, Civil

Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements

De-Cheng Feng et al.

Summary: This paper introduces a practical and comprehensive implementation of ensemble methods for predicting the shear strength of deep beams with or without web reinforcements. The study utilizes four typical ensemble machine learning models and demonstrates their superior performance over traditional machine learning methods in predicting accuracy and discrepancy.

ENGINEERING STRUCTURES (2021)

Article Engineering, Civil

Response estimation of reinforced concrete shear walls using artificial neural network and simulated annealing algorithm

Mohammad Sadegh Barkhordari et al.

Summary: The research utilizes a hybrid technique of artificial neural network (ANN) and Simulated Annealing (SA) to predict the response of reinforced concrete shear walls. By comparing 14 learning algorithms, the best ANN model shows high capability in predicting the responses of RC shear walls.

STRUCTURES (2021)

Article Construction & Building Technology

Mechanics-Guided Genetic Programming Expression for Shear-Strength Prediction of Squat Reinforced Concrete Walls with Boundary Elements

Ahmed Gondia et al.

JOURNAL OF STRUCTURAL ENGINEERING (2020)

Article Engineering, Geological

Shear-flexure-interaction models for planar and flanged reinforced concrete walls

Kristijan Kolozvari et al.

BULLETIN OF EARTHQUAKE ENGINEERING (2019)

Article Computer Science, Interdisciplinary Applications

Prediction of shear strength for squat RC walls using a hybrid ANN-PSO model

X. L. Chen et al.

ENGINEERING WITH COMPUTERS (2018)

Article Engineering, Civil

General solution for shear strength estimate of RC elements based on panel response

Leonardo M. Massone et al.

ENGINEERING STRUCTURES (2018)

Article Engineering, Civil

Probabilistic development of shear strength model for reinforced concrete squat walls

Chao-Lie Ning et al.

EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS (2017)

Article Engineering, Civil

Seismic fragility and reliability of structures isolated by friction pendulum devices: seismic reliability-based design (SRBD)

Paolo Castaldo et al.

EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS (2017)

Article Construction & Building Technology

Cyclic Loading Test for Reinforced Concrete Walls (Aspect Ratio 2.0) with Grade 550 MPa (80 ksi) Shear Reinforcing Bars

Jang-Woon Baek et al.

ACI STRUCTURAL JOURNAL (2017)

Article Construction & Building Technology

Cyclic Shear Behavior of High-Strength Concrete Structural Walls

Susanto Teng et al.

ACI STRUCTURAL JOURNAL (2016)

Article Construction & Building Technology

Experimental study and numerical model calibration of full-scale superimposed reinforced concrete walls with I-shaped cross sections

Xun Chong et al.

ADVANCES IN STRUCTURAL ENGINEERING (2016)

Article Engineering, Civil

Shear strength of squat walls: A strut-and-tie model and closed-form design formula

Wael Kassem

ENGINEERING STRUCTURES (2015)

Article Engineering, Civil

RC shear walls: Full-scale cyclic test, insights and derived analytical model

Adrian Beko et al.

ENGINEERING STRUCTURES (2015)

Article Engineering, Civil

URM Walls Strengthened with Fabric-Reinforced Cementitious Matrix Composite Subjected to Diagonal Compression

Saman Babaeidarabad et al.

JOURNAL OF COMPOSITES FOR CONSTRUCTION (2014)

Article Engineering, Civil

Prediction of punching shear strength of two-way slabs

Ahmed A. Elshafey et al.

ENGINEERING STRUCTURES (2011)

Article Engineering, Civil

Experimental investigation of composite shear walls under shear loadings

A. Arabzadeh et al.

THIN-WALLED STRUCTURES (2011)

Article Engineering, Civil

Evaluation of hysteretic response and strength of repaired R/C walls strengthened with FRPs

Konstantinos K. Antoniades et al.

ENGINEERING STRUCTURES (2007)

Article Engineering, Geological

Damaging potential of low-magnitude near-field earthquakes on low-rise shear walls

M Brun et al.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2004)

Article Engineering, Civil

A simple shear wall model taking into account stiffness degradation

M Brun et al.

ENGINEERING STRUCTURES (2003)

Article Construction & Building Technology

Strength prediction for discontinuity regions by softened strut-and-tie model

SJ Hwang et al.

JOURNAL OF STRUCTURAL ENGINEERING (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)