Optimizing machine learning algorithms for improving prediction of bridge deck deterioration: A case study of Ohio bridges

A Rashidi Nasab, H Elzarka - Buildings, 2023 - mdpi.com
The deterioration of a bridge's deck endangers its safety and serviceability. Ohio has
approximately 45,000 bridges that need to be monitored to ensure their structural integrity …

A review on computational intelligence methods for modelling of light weight composite materials

N Amor, MT Noman, M Petru, N Sebastian… - Applied Soft …, 2023 - Elsevier
Light weight composite materials (LWCM) have gained tremendous attention, thanks to their
low cost, eco-friendly nature, biodegradability, life-cycle superiority, noble mechanical …

A machine learning approach for the estimation of total dissolved solids concentration in lake mead using electrical conductivity and temperature

GE Adjovu, H Stephen, S Ahmad - Water, 2023 - mdpi.com
Total dissolved solids (TDS) concentration determination in water bodies is sophisticated,
time-consuming, and involves expensive field sampling and laboratory processes. TDS …

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

A Kumar, HC Arora, NR Kapoor, K Kumar… - Scientific reports, 2023 - nature.com
The ability of machine learning (ML) techniques to forecast the shear strength of corroded
reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques …

The determination of limit wheel profile for hunting instability of railway vehicles using stacking feature deep forest

X Dai, S Qu, C Huang, P Wu - Engineering Applications of Artificial …, 2023 - Elsevier
Wheel and rail profiles have significant impacts on the vehicle system dynamics. Improper
wheel and rail profiles lead to the decay of vehicle dynamics performance, hunting instability …

Shear capacity prediction for FRCM-strengthened RC beams using Hybrid ReLU-Activated BPNN model

RK Tipu, V Batra, KS Pandya, VR Panchal - Structures, 2023 - Elsevier
This study presents a robust Hybrid ReLU-Activated Backpropagation Neural Network
(BPNN) model for predicting shear strength (VFRCM) in RC beams reinforced with Fiber …

Machine learning-based flexural capacity prediction of corroded RC beams with an efficient and user-friendly tool

A Abushanab, TG Wakjira, W Alnahhal - Sustainability, 2023 - mdpi.com
Steel corrosion poses a serious threat to the structural performance of reinforced concrete
(RC) structures. Thus, this study evaluates the flexural capacity of RC beams through …

Using artificial intelligence approach for investigating and predicting yield stress of cemented paste backfill

VQ Tran - Sustainability, 2023 - mdpi.com
The technology known as cemented paste backfill (CPB) has gained considerable
popularity worldwide. Yield stress (YS) is a significant factor considered in the assessment of …

Adaboosting graph attention recurrent network: A deep learning framework for traffic speed forecasting in dynamic transportation networks with spatial-temporal …

Y Zhang, X Wang, J Yu, T Zeng, J Wang - Engineering Applications of …, 2024 - Elsevier
In construction engineering, transportation is a key factor affecting the construction schedule,
and Transportation Speed Prediction (TSP) provides essential information for the precise …

Ensemble learning based sustainable approach to carbonate reservoirs permeability prediction

DA Musleh, SO Olatunji, AA Almajed, AS Alghamdi… - Sustainability, 2023 - mdpi.com
Permeability is a crucial property that can be used to indicate whether a material can hold
fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and …