#REM DIRECTIONAL DRILLING FREE#

This 250-mile project, if built, will be one of the longest pipeline projects in the world. This paper is part of an Environmental Impact Assessment (EIA) analyses for feasibility study of UFT from Port of Houston to Dallas. UFT uses close-fitting vehicles carrying freight through underground pipelines between terminals. Underground freight transportation (UFT) is considered to increase freight transportation capacity in Texas in next 25 years. The UFT system uses automated technology to carry individual freight vehicles through pipelines under available right-of-way (ROW) of highways. This paper presents environmental impacts of pipeline construction for Underground Freight Transportation (UFT). Pipeline designers, engineers, and decision makers can use methodology provided in this study to choose the best method of construction with less unfavorable environmental impacts between trenchless technology and cut-and-cover methods. The EIS describes and summarizes the environmental and social impacts of pipeline construction. This paper is part of an environmental impact assessment (EIA) analyses for feasibility study of UFT from Port of Houston to Dallas. This paper presents environmental impacts of pipeline construction for underground freight transportation (UFT). Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects.

#REM DIRECTIONAL DRILLING FREE#

Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology.

Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. This paper presents a comprehensive risk identification process for HDD method, by dividing risks into four categories drilling fluid, soil, equipment and pipe, and operation and management to help Project Managers to have comprehensive checklist for their response strategy plan.

This necessitates the development of risk identification guidelines to ensure that contractors monitor the most probable risk and manage its positive and negative impacts effectively and efficiently. The objective of risk identification is to understand what is at risk within the context of the project's explicit and implicit objectives and to generate a comprehensive inventory of risks based on the threats and events that might prevent, degrade, delay or enhance the achievement of project objectives. The primary steps to address risk assessment are risk identification and risk log documentation. Due to work being done underground blindly, it is important to conduct a risk evaluation prior to actual construction. HDD is defined as a steerable system for the installation of pipes, conduits and cables in a shallow arc using a surface-balanced drilling rig. Horizontal Directional Drilling (HDD) has become a favorable method of pipeline installation in urban areas and for crossing obstacles such as rivers and roads.

Rem directional drilling