Impact of Symmetric Vertical Sinusoid Alignments on Infrastructure Construction Costs: Optimizing Energy Consumption in Metropolitan Railway Lines Using Artificial Neural Networks

Abstract

Minimizing energy consumption is a key issue from both an environmental and economic perspectives for railways systems; however, it is also important to reduce infrastructure construction costs. In the present work, an artificial neural network (ANN) was trained to estimate the energy consumption of a metropolitan railway line. This ANN was used to test hypothetical vertical alignments scenarios, proving that symmetric vertical sinusoid alignments (SVSA) can reduce energy consumption by up to 18.4% compared with a flat alignment. Finally, we analyzed the impact of SVSA application on infrastructure construction costs, considering different scenarios based on top–down excavation methods. When balancing reduction in energy consumption against infrastructure construction costs between SVSA and flat alignment, the extra construction costs due to SVSA have a return period of 25–300 years compared with a flat alignment, depending on the soil type and construction method used. Symmetric vertical sinusoid alignment layouts are thus suitable for scattered or soft soils, up to compacted intermediate geomaterials.

Backward Integration as a Business Strategy to Increase Company Profitability

“…Sambeng PreCast has already operated since March 2020 and successfully cut down the company cost in terms of construction cost. It is also essential to reduce infrastructure construction costs (Pineda-Jaramillo et al, 2020). Sambeng PreCast owns three production machines to produce light brick, paving blocks, and precast u-ditch.…”

Predicting the traction power of metropolitan railway lines using different machine learning models

“…Alternatively, some authors have proposed to modify rolling stock characteristics such as the implementation of regenerative brake [9], on board storage systems [10] and considering train load variations and delays [11]. Moreover, other authors have focused on optimising track geometry [12][13][14][15].…”

“…On the other hand, Chen et al [31] established ANN models to calculate the position of multiple trains. Similarly, Pineda-Jaramillo et al [14] developed an ANN using consumption data measured in MetroValencia to estimate the energy consumption of the train, later used the ANN for testing hypothetical operational scenarios aimed to reduce the energy consumption of a metro system, including different vertical alignments, and then analysed the impact of the construction costs of these vertical alignments.…”