A 3D Model for Optimizing Infrastructure Costs in Road Design

Abstract

In this article, the optimal design of a road joining two terminals is investigated. A geometric model is proposed including horizontal transition curves and vertical curves, obtaining parameterizations for the central axis of the road as well as for its entire surface. These parameterizations allow to express and compute, with great simplicity, the major infrastructure costs, including land acquisition, clearance, pavement, maintenance, and earthwork, where multiple layers of materials with different costs can be handled. The road design problem is formulated as a smooth constrained optimization problem and a two-stage algorithm is suggested for its numerical resolution. Finally, numerical results are presented in an academic test and in a case study that propose designing a bypass in a Spanish national road (N-640) to avoid crossing Monterroso's town center.

Classical Planning Model‐Based Approach to Automating Construction Planning on Earthwork Projects

“…To earthwork planning specifically, the majority of existing methods focus on earthwork allocation (Easa, 1987;Hare et al, 2011;Ji et al, 2010) and road alignment design optimization (Vázquez-Méndez et al, 2018;Jong et al, 2000). Such methods deal with the selection of cut and fill cell pairs but ignore the existence of potential TSCs.…”

“…Researchers also investigated the use of images and videos for earthwork volume takeoff (Siebert and Teizer, ) and productivity analysis (Bugler et al, ; Rezazadeh Azar et al, ). Such information provides valuable inputs for adaptive earthmoving operations simulation (Montaser et al, ), earthwork allocation optimization (Easa, ; Hare et al, ; Ji et al, ), and highway design optimization (Vázquez‐Méndez et al, ; Jong et al., ). However, the majority of simulation models generally assumes a predefined project execution plan; thus simplifying the simulation to a cycle between a loading area and a dumping pit (Shi and AbouRizk, ; Li et al., ).…”

“…Automated planner bridges the gap between data and project network and enables semiautomated project planning. (Montaser et al, 2014), earthwork allocation optimization (Easa, 1987;Hare et al, 2011;Ji et al, 2010), and highway design optimization (Vázquez-Méndez et al, 2018;Jong et al, 2000). However, the majority of simulation models generally assumes a predefined project execution plan; thus simplifying the simulation to a cycle between a loading area and a dumping pit (Shi and AbouRizk, 1998;Li et al, 2016a).…”

A three‐dimensional distance transform for optimizing constrained mountain railway alignments

“…The usual goal of alignment optimization is to find an alignment with the lowest comprehensive cost between two given endpoints. In the literature, representative methods for optimizing alignments include particle swarm optimization (Shafahi & Bagherian, 2013;Babapour, Naghdi, Ghajar, & Mortazavi, 2018;Pu et al, 2019), two-stage method that combines global optimization methods with a gradient type algorithm (Vázquez-Méndez, Casal, Santamarina, & Castro, 2018), derivative-free algorithms (Mondal, Lucet, & Hare, 2015), discrete algorithms (Hirpa, Hare, Lucet, Pushak, & Tesfamariam, 2016;, dynamic programming (Hogan, 1973;Li, Pu, Zhao, & Liu, 2013), mixed integer programming (Easa & Mehmood, 2008), linear programming (Revelle, Whitlatch, & Wright, 1996;Chapra & Canale, 2006), network optimization (Trietsch, 1987a(Trietsch, , 1987b), heuristic neighborhood search with mixed integer programming (Cheng & Lee, 2006;Lee, Tsou, & Liu, 2009), calculus of variations (Howard, Bramnick, & Shaw, 1968), numerical search (Robinson, 1973), enumeration (Easa, 1988), average-end-area method for improving earthwork calculation accuracy from 2D to 3D (Cheng & Jiang, 2013), genetic algorithms (Maji & Jha, 2009, and distance transforms (DTs) (de Smith, 2006;Li et al, 2016;Li et al, 2017;Pu et al, 2018).…”

Mountain railway alignment optimization considering geological impacts: A cost‐hazard bi‐objective model

“…Ideally, railway alignments should bypass existing spatial geological hazards to thoroughly avoid their disastrous impacts. However, alignment design is a complex process that should consider many factors, such as topography (Li, Pu, Schonfeld, Zhang, & Zheng, 2016;Yi, 2018), geology (Karlson, Karlsson, Mörtberg, Olofsson, & Balfors, 2016), environment (Kang, Jha, & Schonfeld, 2012), ecology (Davey, Dunstall, & Halgamuge, 2017), construction (Vázquez-Méndez, Casal, Santamarina, & Castro, 2018), sociology (Yang, Kang, Schonfeld, & Jha, 2014), and economy and safety (Jha & Schonfeld, 2004), to find the optimal solution among the infinite potential alignment alternatives (Shafahi & Bagherian, 2013), while handling multiple constraints (Li et al, 2019). Thus, the resulting optimized alignment solution, which takes into account various design factors, is very likely to intersect with specific geological hazard regions.…”