Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key
parameter for the successful accomplishment of a tunneling project, and the proper and …
parameter for the successful accomplishment of a tunneling project, and the proper and …
Concrete compressive strength using artificial neural networks
PG Asteris, VG Mokos - Neural Computing and Applications, 2020 - Springer
The non-destructive testing of concrete structures with methods such as ultrasonic pulse
velocity and Schmidt rebound hammer test is of utmost technical importance. Non …
velocity and Schmidt rebound hammer test is of utmost technical importance. Non …
Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM
Accurate prediction of TBM performance is very important for efficient completion of TBM
construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring …
construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring …
Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns
The type of materials used in designing and constructing structures significantly affects the
way the structures behave. The performance of concrete and steel, which are used as a …
way the structures behave. The performance of concrete and steel, which are used as a …
Ground improvement and its role in carbon dioxide reduction: a review
Environmental global issues affecting global warming, such as carbon dioxide (CO 2), have
attracted the attention of researchers around the world. This paper reviews and discusses …
attracted the attention of researchers around the world. This paper reviews and discusses …
An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures
The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing
this type of retaining structures. On the other hand, the feasibility of artificial intelligence …
this type of retaining structures. On the other hand, the feasibility of artificial intelligence …
Gaussian process regression technique to estimate the pile bearing capacity
A commonly-encountered problem in foundation design is the reliable prediction of the pile
bearing capacity (PBC). This study is planned to propose a feasible soft computing …
bearing capacity (PBC). This study is planned to propose a feasible soft computing …
On the use of neuro-swarm system to forecast the pile settlement
DJ Armaghani, PG Asteris, SA Fatemi… - Applied Sciences, 2020 - mdpi.com
In civil engineering applications, piles (deep foundations) are pushed into the ground in
order to perform as steady support of structures. As these type of foundations are able to …
order to perform as steady support of structures. As these type of foundations are able to …
Assessment of fine-grained soil compaction parameters using advanced soft computing techniques
The compaction parameters are the most important parameters for any civil engineering
project. In this work, the sand content, fine content, liquid limit, plastic limit, and plasticity …
project. In this work, the sand content, fine content, liquid limit, plastic limit, and plasticity …
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical …
E Yaghoubi, E Yaghoubi, A Khamees… - Neural Computing and …, 2024 - Springer
Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …
learning (EL) are four outstanding approaches that enable algorithms to extract information …