☆ 4.7 Article

Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach

ADVANCED ENGINEERING INFORMATICS (2023)

Related references

Note: Only part of the references are listed.

Article Computer Science, Interdisciplinary Applications

Dual attention deep learning network for automatic steel surface defect segmentation

Y. Pan et al.

Summary: A dual attention deep learning network is developed to classify and locate three types of steel defects on the steel surface in an automatic and accurate manner. Experimental results show that DAN-DeepLabv3+ based on Xception backbone exhibits the best segmentation performance, with F1 scores reaching 86.90%, 99.20%, and 92.81% for the three types of defects.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2022)

Add to Collection

Article Construction & Building Technology

Time-series prediction of shield movement performance during tunneling based on hybrid model

Song-Shun Lin et al.

Summary: This study proposes a hybrid model based on the PSO algorithm and LSTM neural network, exploring automatic data collection and model application in tunnel excavation. By analyzing the relationships between influential factors and predicted object, and testing with 1500 data sets, the hybrid model with all factors performed the best, providing guidance for coping with measured data from an automatic monitoring system in shield machines.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2022)

Add to Collection

Article Construction & Building Technology

Sewer defect detection from 3D point clouds using a transformer-based deep learning model

Yunxiang Zhou et al.

Summary: This research develops a deep learning method called TransPCNet for 3D point cloud defect classification. TransPCNet achieves more accurate and effective results by enhancing feature extraction and learning capability, and introducing a novel loss function to address data imbalance issues.

AUTOMATION IN CONSTRUCTION (2022)

Add to Collection

Article Engineering, Geological

Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network

Nan Zhang et al.

Summary: This paper introduces an intelligent framework for real-time prediction of trajectory deviations in EPB tunnelling, utilizing a hybrid model combining PCA and GRU. The proposed PCA-GRU model demonstrates higher precision in predicting shield movement trajectories compared to other machine learning models, providing a promising solution for real-time trajectory prediction in EPB tunnelling.

ACTA GEOTECHNICA (2022)

Add to Collection

Article Construction & Building Technology

Multi-objective optimization for improved project management: Current status and future directions

Kai Guo et al.

Summary: This paper presents a systematic review on the application of multi-objective optimization (MOO) in project improvement in the construction industry. The study reveals an increasing number of MOO-related papers in recent years, with wide applications in project planning, construction automation, and structural health monitoring. However, challenges such as incompatibility with dynamic features, ambiguity of input-output relationship, and low interaction still exist for the adoption of MOO in project management.

AUTOMATION IN CONSTRUCTION (2022)

Add to Collection

Article Geosciences, Multidisciplinary

Advanced prediction of tunnel boring machine performance based on big data

Jinhui Li et al.

Summary: A machine learning model was developed to predict the TBM performance in real-time using big data obtained from a tunnel project in China, showing superior performance compared to classical theoretical models. Filtering unnecessary parameters was proposed to enhance accuracy and computational efficiency. The study also discussed the impact of data deficiency on model accuracy and suggested supplementing highly correlated parameters to improve prediction.

GEOSCIENCE FRONTIERS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Prediction of TBM penetration rate based on Monte Carlo-BP neural network

Meng Wei et al.

Summary: This study utilizes the BP neural network model and Monte Carlo method to establish a prediction model for TBM driving speed, achieving more accurate and practical predictions. The prediction method combining genetic algorithm with BP neural network has high reference value for predicting TBM driving speed.

NEURAL COMPUTING & APPLICATIONS (2021)

Add to Collection

Article Construction & Building Technology

Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach

Xianlei Fu et al.

Summary: This research presents a spatio-temporal approach for real-time forecasting of TBM operating parameters, using a deep learning model trained on real-time operational data. Global sensitivity analysis using the Sobol method identifies thrust and torque as the most influential factors, with historical penetration rate data critical for accurate forecasting. Further studies could focus on backward optimization to enhance TBM performance based on prediction results.

AUTOMATION IN CONSTRUCTION (2021)

Add to Collection

Article Chemistry, Analytical

Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

Boning He et al.

Summary: An adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time, achieving higher performance and accuracy. The dual-rate information fusion and ANFIS model help in smooth attitude prediction and solve the issue of signal loss in laser targeting systems.

SENSORS (2021)

Add to Collection

Article Construction & Building Technology

Multi-step-ahead prediction of thermal load in regional energy system using deep learning method

Yakai Lu et al.

Summary: The proposed temporal attention encoder-decoder network (TA-EDN) model significantly improves the accuracy of multi-step-ahead thermal load prediction for building energy systems, with validation experiments showing a mean absolute percentage error of 7.4%.

ENERGY AND BUILDINGS (2021)

Add to Collection

Article Engineering, Marine

A BiLSTM hybrid model for ship roll multi-step forecasting based on decomposition and hyperparameter optimization

Yunyu Wei et al.

Summary: This study proposes a new hybrid multi-step ship's roll motion forecasting model, which combines three methodologies and demonstrates superior prediction accuracy and strong robustness.

OCEAN ENGINEERING (2021)

Add to Collection

Article Engineering, Civil

Damage identification for bridge structures based on correlation of the bridge dynamic responses under vehicle load

Yifeng Zhang et al.

Summary: A method based on vehicle-induced response correlation of the bridge for damage identification is proposed in this study. It defines damage indexes based on various correlation coefficients to identify and locate damage effectively. Numerical simulation and experimental validation show that the index based on Pearson correlation coefficient is the most effective.

STRUCTURES (2021)

Add to Collection

Article Engineering, Marine

A novel MP-LSTM method for ship trajectory prediction based on AIS data

Da-wei Gao et al.

Summary: Accurate prediction of ship trajectory is crucial in maritime transportation, with multi-step prediction gaining attention for its ability to predict time and position information in the future period. To overcome the complexity and low accuracy of existing methods, a physical hypothesis is introduced to balance the two, resulting in higher prediction accuracy. The proposed method combines the advantages of TPNet and LSTM, involving AIS data preprocessing, destination and support point solutions, and uncertainty analysis.

OCEAN ENGINEERING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque

Gang Shi et al.

Summary: This study proposes a novel hybrid multi-step prediction model based on VMD-EWT-LSTM, which accurately predicts the cutterhead torque of shield tunneling machine in multiple time steps and demonstrates higher accuracy compared to other methods.

KNOWLEDGE-BASED SYSTEMS (2021)

Add to Collection

Proceedings Paper Engineering, Environmental

Bridge structural damage identification based on parallel CNN-GRU

J. Z. Zou et al.

Summary: Deep learning has been widely used in structural damage identification, and the combination of CNN and GRU models for feature extraction has shown significantly better performance in experiments for structural damage identification.

2ND INTERNATIONAL CONFERENCE ON ADVANCES IN CIVIL AND ECOLOGICAL ENGINEERING RESEARCH (2021)

Add to Collection

Article Engineering, Civil

Distinct element modeling of rock fragmentation by TBM cutter

Mingjing Jiang et al.

EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING (2020)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Concrete bridge surface damage detection using a single-stage detector

Chaobo Zhang et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2020)

Add to Collection

Article Engineering, Aerospace

A direct random sampling method for the Fourier amplitude sensitivity test of nonuniformly distributed uncertainty inputs and its application in C/C nozzles

Bo Gao et al.

AEROSPACE SCIENCE AND TECHNOLOGY (2020)

Add to Collection

Article Construction & Building Technology

Automated Detection of Surface Cracks and Numerical Correlation with Thermal-Structural Behaviors of Fire Damaged Concrete Beams

Eunmi Ryu et al.

INTERNATIONAL JOURNAL OF CONCRETE STRUCTURES AND MATERIALS (2020)

Add to Collection

Article Engineering, Civil

As-Encountered Prediction of Tunnel Boring Machine Performance Parameters using Recurrent Neural Networks

Kabir Nagrecha et al.

TRANSPORTATION RESEARCH RECORD (2020)

Add to Collection

Article Engineering, Civil

Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation

Shuai Gao et al.

JOURNAL OF HYDROLOGY (2020)

Add to Collection

Article Computer Science, Information Systems

Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

Qiang Wang et al.

IEEE ACCESS (2020)

Add to Collection

Article Operations Research & Management Science

A comprehensive comparative analysis of machine learning models for predicting heating and cooling loads

Eslam Mohammed Abdelkader et al.

DECISION SCIENCE LETTERS (2020)

Add to Collection

Article Computer Science, Information Systems

Real-Time Dynamic Earth-Pressure Regulation Model for Shield Tunneling by Integrating GRU Deep Learning Method With GA Optimization

Min-Yu Gao et al.

IEEE ACCESS (2020)

Add to Collection

Article Construction & Building Technology

Recurrent neural networks for real-time prediction of TBM operating parameters

Xianjie Gao et al.

AUTOMATION IN CONSTRUCTION (2019)

Add to Collection

Article Chemistry, Multidisciplinary

Influence of Shield Attitude Change on Shield-Soil Interaction

Xiang Shen et al.

APPLIED SCIENCES-BASEL (2019)

Add to Collection

Article Construction & Building Technology

Dynamic prediction for attitude and position in shield tunneling: A deep learning method

Cheng Zhou et al.

AUTOMATION IN CONSTRUCTION (2019)

Add to Collection

Article Geochemistry & Geophysics

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Zhi Zhong et al.

GEOPHYSICS (2019)

Add to Collection

Article Chemistry, Multidisciplinary

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

Hai Xu et al.

APPLIED SCIENCES-BASEL (2019)

Add to Collection

Article Anesthesiology

Application of Student's t-test, Analysis of Variance, and Covariance

Prabhaker Mishra et al.

ANNALS OF CARDIAC ANAESTHESIA (2019)

Add to Collection

Article Engineering, Environmental

Modeling Fecal Indicator Bacteria in Urban Waterways Using Artificial Neural Networks

Vasikan Vijayashanthar et al.

JOURNAL OF ENVIRONMENTAL ENGINEERING (2018)

Add to Collection

Article Engineering, Geological

Analysis on the Evolution of Rock Block Behavior During TBM Tunneling Considering the TBM-Block Interaction

Zixin Zhang et al.

ROCK MECHANICS AND ROCK ENGINEERING (2018)

Add to Collection

Article Engineering, Environmental

Modeling Fecal Indicator Bacteria in Urban Waterways Using Artificial Neural Networks

Vasikan Vijayashanthar et al.

JOURNAL OF ENVIRONMENTAL ENGINEERING (2018)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes

S. Freitag et al.

COMPUTERS & STRUCTURES (2018)

Add to Collection

Article Construction & Building Technology

Uncertainty and sensitivity analysis applied to hygrothermal simulation of a brick building in a hot and humid climate

Jeanne Goffart et al.

JOURNAL OF BUILDING PERFORMANCE SIMULATION (2017)

Add to Collection

Article Engineering, Geological

Bayesian prediction of TBM penetration rate in rock mass

Amoussou Coffi Adoko et al.

ENGINEERING GEOLOGY (2017)

Add to Collection

Article Thermodynamics

A performance comparison of sensitivity analysis methods for building energy models

Anh-Tuan Nguyen et al.

BUILDING SIMULATION (2015)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Computing Three-Axis Orientations of a Tunnel-Boring Machine through Surveying Observation Points

Xuesong Shen et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2011)

Add to Collection

Article Engineering, Industrial

Extension of the RBD-FAST method to the computation of global sensitivity indices

Thierry Alex Mara

RELIABILITY ENGINEERING & SYSTEM SAFETY (2009)

Add to Collection

Article Engineering, Geological

Theoretical model of shield behavior during excavation. II: Application

A Sramoon et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2002)

Add to Collection

© Peeref 2019-2024. All rights reserved.