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 …
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
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 …
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
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 …
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
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 …
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 …
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 …
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
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 …
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
Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of
various digenetic properties. Consequently, the permeability calculation of tight rocks is …
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 …
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 …
particle packing theories through machine learning techniques. The existing challenge in …