Explainable ensemble learning data-driven modeling of mechanical properties of fiber-reinforced rubberized recycled aggregate concrete
Colossal amounts of construction and demolition waste (C&D) and waste tires have become
a considerable global environmental concern. To alleviate this issue, it is proposed to use …
a considerable global environmental concern. To alleviate this issue, it is proposed to use …
[HTML][HTML] Explainable ensemble learning predictive model for thermal conductivity of cement-based foam
Cement-based foam has emerged as a strong contender in sustainable construction owing
to its superior thermal and sound insulation properties, fire resistance, and cost …
to its superior thermal and sound insulation properties, fire resistance, and cost …
Prior knowledge‐infused neural network for efficient performance assessment of structures through few‐shot incremental learning
Structural seismic safety assessment is a critical task in maintaining the resilience of existing
civil and infrastructures. This task commonly requires accurate predictions of structural …
civil and infrastructures. This task commonly requires accurate predictions of structural …
Machine learning–assisted drift capacity prediction models for reinforced concrete columns with shape memory alloy bars
Despite notable progress made in predicting the drift capacity of reinforced columns with
steel bars, these techniques and methods are proven inapplicable for accurately predicting …
steel bars, these techniques and methods are proven inapplicable for accurately predicting …
Design-oriented machine-learning models for predicting the shear strength of prestressed concrete beams
LA Bedriñana, J Sucasaca, J Tovar… - Journal of Bridge …, 2023 - ascelibrary.org
The shear behavior of prestressed concrete (PC) beams is a complex problem because
there are many influential parameters involved. Currently, the code-based shear strength of …
there are many influential parameters involved. Currently, the code-based shear strength of …
Predicting the drift capacity of precast concrete columns using explainable machine learning approach
Accurately and reliably predicting the drift capacity (DC) of concrete columns is crucial for
the seismic design and damage evaluation of structures. Despite precast concrete columns …
the seismic design and damage evaluation of structures. Despite precast concrete columns …
Development of data-driven models to predict seismic drift response of RC wall structures: An application of deep neural networks
This research aimed to develop data-driven models using deep neural networks (DNNs) that
can rapidly predict the seismic drift responses of reinforced concrete (RC) wall structures in …
can rapidly predict the seismic drift responses of reinforced concrete (RC) wall structures in …
Integrating Machine Learning Models into Building Codes and Standards: Establishing Equivalence through Engineering Intuition and Causal Logic
MZ Naser - Journal of Structural Engineering, 2024 - ascelibrary.org
The traditional approach to formulating building codes often is slow and labor-intensive, and
may struggle to keep pace with the rapid evolution of technology and domain findings …
may struggle to keep pace with the rapid evolution of technology and domain findings …
No more black-boxes: estimate deformation capacity of non-ductile RC shear walls based on generalized additive models
Abstract Machine learning techniques have gained attention in earthquake engineering for
their accurate predictions, but their opaque black-box models create ambiguity in the …
their accurate predictions, but their opaque black-box models create ambiguity in the …
Over-sampling for data augmentation in data-driven models for the shear strength prediction of RC membranes
LA Bedriñana, JG Landeo, JC Sucasaca… - Structures, 2024 - Elsevier
Complex reinforced concrete (RC) structures are generally assessed as a group of
individual membrane elements subjected to in-plane combined stresses; however, an …
individual membrane elements subjected to in-plane combined stresses; however, an …