Recent trends in prediction of concrete elements behavior using soft computing (2010–2020)
M Mirrashid, H Naderpour - Archives of Computational Methods in …, 2021 - Springer
Soft computing (SC), due to its high abilities to solve the complex problems with uncertainty
and multiple parameters, has been widely investigated and used, especially in structural …
and multiple parameters, has been widely investigated and used, especially in structural …
Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network
H Nhat-Duc, QL Nguyen, VD Tran - Automation in Construction, 2018 - Elsevier
Crack detection is a crucial task in periodic pavement survey. This study establishes and
compares the performance of two intelligent approaches for automatic recognition of …
compares the performance of two intelligent approaches for automatic recognition of …
Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer …
In many engineering projects, the soil compression coefficient is an important parameter
used for estimating the settlement of soil layers. The common practice of determining the soil …
used for estimating the settlement of soil layers. The common practice of determining the soil …
An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter …
ND Hoang - Advances in Civil Engineering, 2018 - Wiley Online Library
This study establishes an artificial intelligence (AI) model for detecting pothole on asphalt
pavement surface. Image processing methods including Gaussian filter, steerable filter, and …
pavement surface. Image processing methods including Gaussian filter, steerable filter, and …
A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data
Flash floods are widely recognized as one of the most devastating natural hazards in the
world, therefore prediction of flash flood-prone areas is crucial for public safety and …
world, therefore prediction of flash flood-prone areas is crucial for public safety and …
GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method
Forest fire danger map at regional scale is considered of utmost importance for local
authority to efficiently allocate its resources to fire prevention measures and establish …
authority to efficiently allocate its resources to fire prevention measures and establish …
Convolutional neural network-Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups
Microalgae are single-celled organisms that have been extensively utilized in
biotechnology, pharmacology and foodstuff in recent years. The description and …
biotechnology, pharmacology and foodstuff in recent years. The description and …
Estimating compressive strength of high performance concrete with Gaussian process regression model
This research carries out a comparative study to investigate a machine learning solution that
employs the Gaussian Process Regression (GPR) for modeling compressive strength of …
employs the Gaussian Process Regression (GPR) for modeling compressive strength of …
A review on artificial intelligence applications for facades
This review applies a transformer-based topic model to reveal trends and relationships in
Artificial Intelligence (AI)-driven facade research, with a focus on architectural …
Artificial Intelligence (AI)-driven facade research, with a focus on architectural …
Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi …
ND Hoang, DT Bui - Bulletin of Engineering Geology and the Environment, 2018 - Springer
Assessment of the earthquake-induced liquefaction potential is a critical concern in design
processes of construction projects. This study proposes a novel soft computing model with a …
processes of construction projects. This study proposes a novel soft computing model with a …