Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches
Accurately predicting the Modulus of Resilience (MR) of subgrade soils, which exhibit non-
linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory …
linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory …
Indirect estimation of resilient modulus (Mr) of subgrade soil: Gene expression programming vs multi expression programming
Accurate prediction of resilient modulus (MR) in compacted subgrade soil is crucial for
planning secure and environmentally friendly flexible pavement systems. This research …
planning secure and environmentally friendly flexible pavement systems. This research …
Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP
The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its
superior properties in comparison to normal concrete such as compaction without vibration …
superior properties in comparison to normal concrete such as compaction without vibration …
[HTML][HTML] Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms
Contemporary infrastructure requires structural elements with enhanced mechanical
strength and durability. Integrating nanomaterials into concrete is a promising solution to …
strength and durability. Integrating nanomaterials into concrete is a promising solution to …
RGR-Net: Refined Graph Reasoning Network for multi-height hotspot defect detection in photovoltaic farms
Unmanned aerial vehicle (UAV) detection of hotspot defects at multi-height is crucial for the
reliable operation of photovoltaic (PV) farms. However, there are two major challenges in PV …
reliable operation of photovoltaic (PV) farms. However, there are two major challenges in PV …
Compressive strength of nano concrete materials under elevated temperatures using machine learning
In this study, four Artificial intelligence (AI)-based machine learning models were developed
to estimate the Residual compressive strength (RCS) value of concrete supported with nano …
to estimate the Residual compressive strength (RCS) value of concrete supported with nano …
Predicting natural vibration period of concrete frame structures having masonry infill using machine learning techniques
The natural period of vibration is one of the most significant factors used in the seismic
design of buildings. Although the building design codes and previous studies provide some …
design of buildings. Although the building design codes and previous studies provide some …
Analyzing the efficacy of waste marble and glass powder for the compressive strength of self-compacting concrete using machine learning strategies
QT Guan, ZL Tong, MN Amin, B Iftikhar… - Reviews on Advanced …, 2024 - degruyter.com
Self-compacting concrete (SCC) is well-known for its capacity to flow under its own weight,
which eliminates the need for mechanical vibration and provides benefits such as less labor …
which eliminates the need for mechanical vibration and provides benefits such as less labor …
[HTML][HTML] Prediction models for the hybrid effect of nano materials on radiation shielding properties of concrete exposed to elevated temperatures
In modern construction, nanomaterials can be added to concrete to improve its radiation
shielding properties. A prediction model for the gamma-ray radiation shielding properties …
shielding properties. A prediction model for the gamma-ray radiation shielding properties …
[HTML][HTML] Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures
HA Dahish, AD Almutairi - Results in Engineering, 2025 - Elsevier
The addition of nanomaterials to concrete is widely employed in modern construction to
improve its durability and mechanical properties. In the present study, two machine learning …
improve its durability and mechanical properties. In the present study, two machine learning …