T. Iseley

发表

Leakage detection techniques for oil and gas pipelines: State-of-the-art

Hongfang Lu, T. Iseley, Saleh Behbahani, 2020 .

Vertical tunneling in China-A case study of a hydraulic tunnel in Beihai

T. Iseley, G. Wei, Xiao Wang, 2021 .

A jacking force study of curved steel pipe roof in Gongbei tunnel: Calculation review and monitoring data analysis

Peng Zhang, B. Ma, T. Iseley, 2017 .

Research on the structures and hydraulic performances of the typical direct jet nozzles for water jet technology

X. Pei, T. Iseley, S. Behbahani, 2019, Journal of the Brazilian Society of Mechanical Sciences and Engineering.

A Feature Selection–Based Intelligent Framework for Predicting Maximum Depth of Corroded Pipeline Defects

Hongfang Lu, Niannian Wang, T. Iseley, 2022, Journal of Performance of Constructed Facilities.

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Gang Wei, Tom Iseley, Hongfang Lu, 2020, IEEE Access.

Novel Data-Driven Framework for Predicting Residual Strength of Corroded Pipelines

Hongfang Lu, T. Iseley, J. Matthews, 2021 .

CHALLENGING ISSUES IN MODELING DETERIORATION OF COMBINED SEWERS

Dulcy M. Abraham, Tom Iseley, Reini Wirahadikusumah, 2001 .

Assessment technologies for sewer system rehabilitation

Dulcy M. Abraham, Tom Iseley, Ravi K. Prasanth, 1998 .

Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine

Tom Iseley, Hongfang Lu, Mohammadamin Azimi, 2019, Energy Reports.

Rehabilitation Alternatives for Concrete and Brick Sewers

Dulcy M. Abraham, Sanjiv Gokhale, Tom Iseley, 2002 .

A multi-objective optimizer-based model for predicting composite material properties

Tom Iseley, Xin Ma, Saleh Behbahani, 2021 .

A risk management approach to safety assessment of trenchless technologies for culvert rehabilitation

Tom Iseley, Myung Jin Chae, Hyung Seok Jeong, 2010 .

Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks

Dulcy M. Abraham, Tom Iseley, Mohammad R. Jahanshahi, 2018, Automation in Construction.

Leveraging Machine Learning for Pipeline Condition Assessment

Hongfang Lu, Niannian Wang, T. Iseley, 2023, Journal of Pipeline Systems Engineering and Practice.

Deep Learning-Based Automated Detection of Sewer Defects in CCTV Videos

Dulcy M. Abraham, Jack Chin Pang Cheng, Tom Iseley, 2020, J. Comput. Civ. Eng..

Hybrid machine learning for pullback force forecasting during horizontal directional drilling

John C. Matthews, Tom Iseley, Hongfang Lu, 2021 .

An ensemble model based on relevance vector machine and multi-objective salp swarm algorithm for predicting burst pressure of corroded pipelines

Tom Iseley, Mohammadamin Azimi, John Matthews, 2021 .

Pipeline Inspection and Health Monitoring Technology

Hongfang Lu, T. Iseley, Lingdi Fu, 2023 .

Water Pipeline Renewal Evaluation Using AWWA Class IV CIPP, Pipe Bursting, and Open-Cut

Tom Iseley, Behnam Hashemi, Jim Raulston, 2011 .

Trenchless Construction Technologies for Oil and Gas Pipelines: State-of-the-Art Review

John C. Matthews, Tom Iseley, Mohammadamin Azimi, 2020 .

National Cooperative Highway Research Program Synthesis of Highway Practice 242 Trenchless Installation of Conduits Beneath Roadways

E. P., T. Iseley, E. P., 2012 .

EXPERIMENTAL INVESTIGATION OF STRESS-STRAIN STATE OF THE THIN-WALLED STEEL PIPE SUBJECTED TO COMBINED LOADS

T. Iseley, O. Kinash, 2008 .

Empirical Assessment of Unobserved Heterogeneity and Polyvinyl Chloride Pipe Failures in Water Distribution Systems

F. Mannering, K. Faust, D. Abraham, 2017 .

Modeling the Frequency of Water Main Breaks in Water Distribution Systems: Random-Parameters Negative-Binomial Approach

Fred L. Mannering, Dulcy M. Abraham, Tom Iseley, 2017 .