Water Distribution System Computer-Aided Design by Agent Swarm Optimization

Optimal design of water distribution systems (WDS), including the sizing of components, quality control, reliability, renewal and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly-dimensional, multimodal, non-linear problems, especially given inaccurate, noisy, discrete and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent based systems. It is aimed at supporting decisionmaking processes by solving multi-objective optimization problems. ASO offers robustness through a framework where various population-based algorithms co-exist. The ASO framework is described and used to solve the optimal design of WDS. The approach allows engineers to work in parallel with the computational algorithms to force the recruitment of new searching elements, thus contributing to the solution process with expert-based proposals.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Spanish Ministry of Education and Science, and ACOMP/2010/146 of the education department of the Generalitat Valenciana. The use of English was revised by John Rawlins.Montalvo Arango, I.; Izquierdo Sebastián, J.; Pérez García, R.; Herrera Fernández, AM. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering. 29(6):433-448. https://doi.org/10.1111/mice.12062S433448296Adeli, H., & Kumar, S. (1995). Distributed Genetic Algorithm for Structural Optimization. Journal of Aerospace Engineering, 8(3), 156-163. doi:10.1061/(asce)0893-1321(1995)8:3(156)Afshar, M. H., Akbari, M., & Mariño, M. A. (2005). 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Use of Heading Direction for Recreating the Horizontal Alignment of an Existing Road

This article proposes a new method for fit- ting the horizontal alignment of a road to a set of (x, y) points. Those points can be obtained from digital im- agery or GPS-data collection. Unlike current methods that represent road alignment through its curvature, the proposed method describes the horizontal alignment as a sequence of headings. An analytic–heuristic approach is introduced. The proposed method produces unique solu- tions even for complex horizontal alignments. Some ex- amples and a case study are presented. This solution may not be accurate enough for road redesign, but it allows researchers and departments of transportation to obtain accurate geometric featuresCamacho Torregrosa, FJ.; Pérez Zuriaga, AM.; Campoy Ungria, JM.; García García, A.; Tarko, A. (2015). Use of Heading Direction for Recreating the Horizontal Alignment of an Existing Road. Computer-Aided Civil and Infrastructure Engineering. 30(4):282-299. doi:10.1111/mice.12094S282299304Awuah-Baffour, R., Sarasua, W., Dixon, K. K., Bachman, W., & Guensler, R. (1997). Global Positioning System with an Attitude: Method for Collecting Roadway Grade and Superelevation Data. Transportation Research Record: Journal of the Transportation Research Board, 1592(1), 144-150. doi:10.3141/1592-17Ben-Arieh, D., Chang, S., Rys, M., & Zhang, G. (2004). Geometric Modeling of Highways Using Global Positioning System Data andB-Spline Approximation. Journal of Transportation Engineering, 130(5), 632-636. doi:10.1061/(asce)0733-947x(2004)130:5(632)Bosurgi, G., & D’Andrea, A. (2012). A Polynomial Parametric Curve (PPC-CURVE) for the Design of Horizontal Geometry of Highways. Computer-Aided Civil and Infrastructure Engineering, 27(4), 304-a312. doi:10.1111/j.1467-8667.2011.00750.xCai, H., & Rasdorf, W. (2008). Modeling Road Centerlines and Predicting Lengths in 3-D Using LIDAR Point Cloud and Planimetric Road Centerline Data. Computer-Aided Civil and Infrastructure Engineering, 23(3), 157-173. doi:10.1111/j.1467-8667.2008.00518.xCastro, M., Iglesias, L., Rodríguez-Solano, R., & Sánchez, J. A. (2006). Geometric modelling of highways using global positioning system (GPS) data and spline approximation. Transportation Research Part C: Emerging Technologies, 14(4), 233-243. doi:10.1016/j.trc.2006.06.004Dong, H., Easa, S. M., & Li, J. (2007). Approximate Extraction of Spiralled Horizontal Curves from Satellite Imagery. Journal of Surveying Engineering, 133(1), 36-40. doi:10.1061/(asce)0733-9453(2007)133:1(36)Easa, S. M., Dong, H., & Li, J. (2007). Use of Satellite Imagery for Establishing Road Horizontal Alignments. Journal of Surveying Engineering, 133(1), 29-35. doi:10.1061/(asce)0733-9453(2007)133:1(29)Hummer , J. E. Rasdorf , W. J. Findley , D. J. Zegeer , C. V. Sundstrom , C. A. 2010 Procedure for Curve Warning Signing, Delineation, and Advisory Speeds for Horizontal Curves http://ntl.bts.gov/lib/38000/38400/38476/2009--07finalreport.pdfImran, M., Hassan, Y., & Patterson, D. (2006). GPS-GIS-Based Procedure for Tracking Vehicle Path on Horizontal Alignments. Computer-Aided Civil and Infrastructure Engineering, 21(5), 383-394. doi:10.1111/j.1467-8667.2006.00444.xLi, Z., Chitturi, M. V., Bill, A. R., & Noyce, D. A. (2012). Automated Identification and Extraction of Horizontal Curve Information from Geographic Information System Roadway Maps. Transportation Research Record: Journal of the Transportation Research Board, 2291(1), 80-92. doi:10.3141/2291-10Othman, S., Thomson, R., & Lannér, G. (2012). Using Naturalistic Field Operational Test Data to Identify Horizontal Curves. Journal of Transportation Engineering, 138(9), 1151-1160. doi:10.1061/(asce)te.1943-5436.0000408Zuriaga, A. M. P., García, A. G., Torregrosa, F. J. C., & D’Attoma, P. (2010). Modeling Operating Speed and Deceleration on Two-Lane Rural Roads with Global Positioning System Data. Transportation Research Record: Journal of the Transportation Research Board, 2171(1), 11-20. doi:10.3141/2171-02Roh, T.-H., Seo, D.-J., & Lee, J.-C. (2003). An accuracy analysis for horizontal alignment of road by the kinematic GPS/GLONASS combination. KSCE Journal of Civil Engineering, 7(1), 73-79. doi:10.1007/bf02841990Shafahi, Y., & Bagherian, M. (2012). A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem. Computer-Aided Civil and Infrastructure Engineering, 28(1), 52-67. doi:10.1111/j.1467-8667.2012.00769.xTsai, Y. (James), Wu, J., Wang, Z., & Hu, Z. (2010). Horizontal Roadway Curvature Computation Algorithm Using Vision Technology. Computer-Aided Civil and Infrastructure Engineering, 25(2), 78-88. doi:10.1111/j.1467-8667.2009.00622.

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. This article presents the first study to systematically analyze the design of all intersections in a large country, based on aerial imagery and deep learning. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high-level features, including the intersection's type, size, shape, lane markings, and complexity, which were used to cluster similar designs. An Australian telematics data set linked infrastructure design to driving behaviors captured during 66 million kilometers of driving. This showed more frequent hard acceleration events (per vehicle) at four- than three-way intersections, relatively low hard deceleration frequencies at T-intersections, and consistently low average speeds on roundabouts. Overall, domain-specific feature extraction enabled the identification of infrastructure improvements that could result in safer driving behaviors, potentially reducing road trauma.Comment: 16 pages, 10 figures. Computer-Aided Civil and Infrastructure Engineering (2020

Digital Twin Technologies towards Understanding the Interactions between Transportation and Other Civil Infrastructure Systems

69A3551747119Digital Twin (DT) technology is the next step in the gradual shift from physical to digital models in civil engineering. Computer-Aided Drafting (CAD) revolutionized the industry by reducing the time and costs associated with documenting design. Building Information Modeling (BIM) has eliminated the need for physical design descriptors (i.e., drawings or physical models). DT models build off CAD and BIM but are utilized over the operational life of the infrastructure as a management tool. A DT is a relevant abstraction of the physical asset; it is most frequently used to model, improve, and control manufacturing systems. Civil engineering applications using DTs have been emerging, but transportation infrastructure represents a challenging extension of DT technology because of its spatial scale, as well as its voluminous and time-varying data. However, DT is a powerful decision support tool for the design, maintenance, and management of transportation infrastructure, particularly for studying its interdependencies with other infrastructure systems, which is of relevance to smart cities. The primary objective of this research was to explore the effectiveness of DT technology as a tool to visualize and understand interactions between transportation and other related civil infrastructure systems

Bi-objective modeling approach for repairing multiple feature infrastructure systems

A bi-objective decision aid model for planning long-term maintenance of infrastructure systems is presented, oriented to interventions on their constituent elements, with two upgrade levels possible for each element (partial/full repairs). The model aims at maximizing benefits and minimizing costs, and its novelty is taking into consideration, and combining, the system/element structure, volume discounts, and socioeconomic factors. The model is tested with field data from 229 sidewalks (systems) and compared to two simpler repair policies, of allowing only partial or full repairs. Results show that the efficiency gains are greater in the lower mid-range budget region. The proposed modeling approach is an innovative tool to optimize cost/benefits for the various repair options and analyze the respective trade-offs.info:eu-repo/semantics/publishedVersio

The Fuzzy Project Scheduling Problem with Minimal Generalized Precedence Relations

In scheduling, estimations are affected by the imprecision of limited information on future events, and the reduction in the number and level of detail of activities. Overlapping of processes and activities requires the study of their continuity, along with analysis of the risks associated with imprecision. In this line, this paper proposes a fuzzy heuristic model for the Project Scheduling Problem with flows and minimal feeding, time and work Generalized Precedence Relations with a realistic approach to overlapping, in which the continuity of processes and activities is allowed in a discretionary way. This fuzzy algorithm handles the balance of process flows, and computes the optimal fragmentation of tasks, avoiding the interruption of the critical path and reverse criticality. 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