The spatiotemporal evolution pattern of urban resilience in the Yangtze River Delta urban agglomeration based on TOPSIS-PSO-ELM

X Chenhong, Z Guofang - Sustainable Cities and Society, 2022 - Elsevier
Urban resilience, a methodology that can quantify the healthy operation of cities, has
theoretical and practical significance for clarifying urban development rules and improving …

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

RM Adnan, RR Mostafa, O Kisi, ZM Yaseen… - Knowledge-Based …, 2021 - Elsevier
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …

Applications of machine learning in mechanised tunnel construction: A systematic review

F Shan, X He, H Xu, DJ Armaghani, D Sheng - Eng, 2023 - mdpi.com
Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their
high efficiency and reliability. The proliferation of data obtained from site investigations and …

Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes

HU Ahmed, AS Mohammed, AA Mohammed, RH Faraj - Plos one, 2021 - journals.plos.org
Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes
as the main binder instead of ordinary Portland cement; this leads to the geopolymer …

Use of interpretable machine learning approaches for quantificationally understanding the performance of steel fiber-reinforced recycled aggregate concrete: From the …

S Zhang, W Chen, J Xu, T Xie - Engineering Applications of Artificial …, 2024 - Elsevier
In this study, four machine learning (ML) algorithms, namely Support Vector Machine (SVM),
Back-propagation Artificial Neural Network (BP-ANN), Adaptive Boosting (AdaBoost), and …

Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning

X Yin, Q Liu, X Huang, Y Pan - Tunnelling and Underground Space …, 2022 - Elsevier
The perception of surrounding rock geological conditions ahead the tunnel face is essential
for TBM safe and efficient tunnelling. This paper developed a perception approach of …

Multi-objective optimization control for tunnel boring machine performance improvement under uncertainty

W Liu, A Li, C Liu - Automation in Construction, 2022 - Elsevier
The tunnel boring machine (TBM) is an important and common construction method for
urban subways, and it requires a detailed and rational control strategy to ensure the safety …

A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates

N Kardani, A Bardhan, B Roy, P Samui… - Engineering with …, 2022 - Springer
Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of
various digenetic properties. Consequently, the permeability calculation of tight rocks is …

A novel combination of PCA and machine learning techniques to select the most important factors for predicting tunnel construction performance

J Wang, AS Mohammed, E Macioszek, M Ali, DV Ulrikh… - Buildings, 2022 - mdpi.com
Numerous studies have reported the effective use of artificial intelligence approaches,
particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues …

Predicting concrete strength through packing density using machine learning models

SNRG Pallapothu, RK Pancharathi, R Janib - Engineering Applications of …, 2023 - Elsevier
This study presents an innovative approach to predict concrete compressive strength using
particle packing theories through machine learning techniques. The existing challenge in …