Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies
M Mishra - Journal of Cultural Heritage, 2021 - Elsevier
This paper performed a systematic review of the various machine learning (ML) techniques
applied to assess the health condition of heritage buildings. More robust predictive models …
applied to assess the health condition of heritage buildings. More robust predictive models …
Application of bio and nature-inspired algorithms in agricultural engineering
The article reviewed the four major Bioinspired intelligent algorithms for agricultural
applications, namely ecological, swarm-intelligence-based, ecology-based, and multi …
applications, namely ecological, swarm-intelligence-based, ecology-based, and multi …
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
This study aims to implement a hybrid ensemble surrogate machine learning technique in
predicting the compressive strength (CS) of concrete, an important parameter used for …
predicting the compressive strength (CS) of concrete, an important parameter used for …
A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
DJ Armaghani, PG Asteris - Neural Computing and Applications, 2021 - Springer
Despite the extensive use of mortars materials in constructions over the last decades, there
is not yet a reliable and robust method, available in the literature, which can estimate its …
is not yet a reliable and robust method, available in the literature, which can estimate its …
Estimating compressive strength of concrete using neural electromagnetic field optimization
Concrete compressive strength (CCS) is among the most important mechanical
characteristics of this widely used material. This study develops a novel integrative method …
characteristics of this widely used material. This study develops a novel integrative method …
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 …
Machine learning models for predicting the compressive strength of concrete containing nano silica
Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a
time-consuming and laborious process. The present study aims to propose surrogate …
time-consuming and laborious process. The present study aims to propose surrogate …
Success and challenges in predicting TBM penetration rate using recurrent neural networks
Abstract Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling
projects. Forecasting future TBM performance would be desirable for project time …
projects. Forecasting future TBM performance would be desirable for project time …
Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms
In this study, artificial intelligence algorithms are proposed for estimating the compressive
strength of hollow concrete block masonry prisms, including neural networks (ANN) …
strength of hollow concrete block masonry prisms, including neural networks (ANN) …
Soft computing-based models for the prediction of masonry compressive strength
Masonry is a building material that has been used in the last 10.000 years and remains
competitive today for the building industry. The compressive strength of masonry is used in …
competitive today for the building industry. The compressive strength of masonry is used in …