Artificial Intelligence applied to the reduction of environmental impact in the construction of high-speed rail infrastructures

ALBERTO, MORAL and FRANCISCO, CAMPO and GREGORIO, SAINZ-PALMERO and JORGE, RODRIGUEZ and JOSE M., BENITEZ and LAURA, PABLOS and MANUEL, PARRA and MARTA, GALENDE and RUBEN, CARNERERO (2017) Artificial Intelligence applied to the reduction of environmental impact in the construction of high-speed rail infrastructures. In: Fifth International Conference on Advances in Civil, Structural and Mechanical Engineering - CSM 2017, 02-03 September, 2017, Zurich, Switzerland.

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Abstract

The development of a society is closely related to the quantity and quality of its infrastructures. Investment in infrastructures such as transport, energy, telecommunications are vital to the development of a country. It is estimated that global demand for mobility and transport infrastructure for 2050 will be raised by 60% compared to 2010 data [1] . This means a very significant increase in the number of kilometers of roads and railways to build in the coming years. The report “ Green House Gases EU Tran sport Emissions: Routes to 2050 ” [2] indicated that 28% of total emissions associated with rail transport are due to infrastructure. Nearly half of these emissions are caused during the infrastructure construction process. Most of these emissions are mainl y due to material production , transport and earthmoving. The construction of sustainable transport infrastructures is a growing priority in the policies of many countries around the world, including aspects such as social, environmental (related to climate change) and economic impact . Efficiency in the construction and management of transport infrastructure are key to sustainable development goals. The transport infrastructure, such as high - speed rail lines poses a major effort in construction and even more , in terms of scheduling and resource management. Proper planning of tasks and resources in construction is essential to improve or optimize the environmental impact. Under the framework of the European pr oject LIFE12 ENV / ES / 000686, “ LCA, environmental footprints and intelligent analysis for the rail infrastructure construction sector ” , a decision support tool has been developed for the implementation phase, which aims to reduce the water and carbon footprints by 5% and 10 % respectively, by the applicat ion of artificial intelligen ce algorithms. The tool is intended for agents involved in the construction of high - speed rail infrastructure. It allows to model the entire work, and , based on multi - objective evolutionary algorithms [3] , to obtain a set of fea sible solutions for scheduling tasks and resources. Each of these solutions is part of the P areto front and quantifies compensations in the satisfaction of the different objectives, or the search for a unique solution that satisfies the subjective preferen ces of a human decision - making. The goal of this work is to provide a multi - objective evolutionary algorithm to optimize the decision - making process an d analysis of building resource - constrained project scheduling for this kind of infrastructures, m inimizi ng the environmental impact.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: intelligent analisys, carbon footprints, water footprints, high-speed train, jobs scheduling, multi-objective, environmental impact
Depositing User: Mr. John Steve
Date Deposited: 11 Mar 2019 08:29
Last Modified: 11 Mar 2019 08:29
URI: http://publications.theired.org/id/eprint/366

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