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This paper presents an optimal sensor placement (OSP) technique designed to be implemented on Structural Health Monitoring (SHM) systems. A steel bridge was modeled in ANSYS environment and four load values were applied at pre-identified locations to generate data. Each experiment yielded an array of data that contains the location, as well as corresponding deformation and safety factors. Measurements were taken at 1,000,000 positions on the bridge and a library of a similar number of failure modes was created for each experiment. Each data library was processed as a multi-dimensional matrix by applying the average filtering algorithm. Local extrema were identified in terms of the corresponding deformation and safety factors by removing repeated values at nearby locations. The results provided a list of 100 locations with maximum deformation or minimum safety factors, containing the optimized positions on the bridge for placement of sensors. The final developed system that includes this placement algorithm capable of simulating multiple load conditions on structures, identifying possible failure points, and detecting and predicting failure scenarios. Both hardware and software implementations of a model of a bridge were performed as a pilot project to validate the proposed system.

This paper presents the design and development of a structural health monitoring (SHM) system specifically tailored for transportation infrastructure components such as bridges. If focuses mainly the application of statistical machine learning (ML) algorithms to classify deformation datasets of a bridge. A model of a steel bridge was constructed and contactless sensors were placed to collect deformation data. Four loads were applied at each of the pre-defined four locations identified to represent heavy loads across the real bridge. Computer simulation in ANSYS and application of gradient boosting neural networks were performed to produce a comparative and predictive analysis of the behavior of transportation infrastructures, which can be used to understand the health of the structure and make informed decisions. Deformation levels at 100 critical locations on the bridge model were collected in each experiment by using sensors. The experiments were repeated to get average data for processing. Python programming language was used for coding and the analysis was performed in a Google Collaboratory Notebook. Development and training of the models were done using the Pycaret, which is a Python based framework that supports a variety of ML tools. Performance of each ML technique was evaluated by means of the accuracy. The final is capable of simulating multiple load conditions on structures, identifying possible failure points, and detecting and predicting failure scenarios. Both hardware and software implementations of a model of a bridge were performed as a pilot project to validate the proposed system.

Tsunami hazard can be analyzed from both deterministic and probabilistic points of view. The deterministic approach is based on a “credible” worst case tsunami, which is often selected from historical events in the region of study. Within the probabilistic approach (PTHA, Probabilistic Tsunami Hazard Analysis), statistical analysis can be carried out in particular regions where historical records of tsunami heights and runup are available. In areas where these historical records are scarce, synthetic series of events are usually generated using Monte Carlo approaches. Commonly, the sea level variation and the currents forced by the tidal motion are either disregarded or considered and treated as aleatory uncertainties in the numerical models. However, in zones with a macro and meso tidal regime, the effect of the tides on the probability distribution of tsunami hazard can be highly important. In this work, we present a PTHA methodology based on the generation of synthetic seismic catalogs and the incorporation of the sea level variation into a Monte Carlo simulation. We applied this methodology to the Bay of Cádiz area in Spain, a zone that was greatly damaged by the 1755 earthquake and tsunami. We build a database of tsunami numerical simulations for different variables: faults, earthquake magnitudes, epicenter locations and sea levels. From this database we generate a set of scenarios from the synthetic seismic catalogs and tidal conditions based on the probabilistic distribution of the involved variables. These scenarios cover the entire range of possible tsunami events in the synthetic catalog (earthquakes and sea levels). Each tsunami scenario is propagated using the tsunami numerical model C3, from the source region to the target coast (Cádiz Bay). Finally, we map the maximum values for a given probability of the selected variables (tsunami intensity measures) producing a set of thematic hazard maps. 1000 different time series of combined tsunamigenic earthquakes and tidal levels were synthetically generated using the Monte Carlo technique. Each time series had a 10000-year duration. The tsunami characteristics were statistically analyzed to derive different thematic maps for the return periods of 500, 1000, 5000, and 10000 years, including the maximum wave elevation, the maximum current speed, the maximum Froude number, and the maximum total forces.

Visibility is one of the most important weather parameters that affect the operation and safety levels of transportation systems. Low visibility conditions provide highly unsafe scenarios on roads, causing accidents and jeopardizing operations. Accurate visibility forecasts play a key role in decision-making and management of transportation systems. However, the complexity and variability of weather variables makes accurate visibility forecasting a major challenge for transportation agencies nationwide. The related machine learning literature for visibility forecasting treats the problem as a classification one. However, the visibility forecasting task as a regression problem is scarce. This paper addresses the task of single step (t+1) visibility forecasting framed as a regression problem using two deep learning models: a multilayer perceptron (MLP) and a convolutional neural network (CNN). Numerical results indicate that the average root mean squared error of the models is around one mile. The CNN achieved the best average results for three hours lag and nine hours lag, while the MLP performed better for a six hours lag.

Smart parking management systems need to keep up with the state of parking spots at all times. This is efficiently accomplished via sensor nodes that detect vehicles and update the system’s state as it changes in real time. When sensor nodes use wireless communication as their primary communication link, the deployment approach becomes important, since it directly affects network connectivity, cost, and lifetime. The main factor to consider when deploying wireless sensor networks (WSN) in parking environments is the prediction of radio frequency (RF) signal propagation. Inaccurate propagation models lead to systems that under- or over-perform, both of which negatively affect WSN performance. Most of the existing RF propagation models are created to support cellular systems environments, which drastically differ from indoor/outdoor parking environments—few or no model exists that accurately predict RF signal propagation in parking environments. Therefore, there is a need for models that accurately characterize RF signal propagation in these environments. This paper proposes empirical path loss models for WSN deployment in indoor and outdoor car parking lots. The proposed models are compared with theoretical models. Theoretical models deviate from the proposed models and the measured values by 10% to 46%. The provided models, as well as the measured data, can be used for efficient planning and deployment of WSN in various proposed smart cities, intelligent transportation, and parking lot systems.

Relation among estuarine processes, mixing mechanisms and turbidity maximum zone (TMZ) formation was evaluated in a microtidal tropical estuary of high fluvial discharge (Magdalena River, northwestern South America). Particularly, assessing the effects of vertical stratification over mixing, estuarine circulation and suspended sediment transport processes. Measurements of salinity, temperature, density, currents and suspended sediment concentration (SSC) were performed at the Magdalena river mouth during high and low streamflows to distinguish among two contrasting seasonal conditions (flow conditions, wind patterns, wave energy). Salinity, temperature, density and location of the freshwater-saltwater interface (FSI) exhibited a sharp seasonal variability. The river mouth and delta front were stratified regardless of the hydrologic season, whereas the riverine section was stratified during the low streamflow season, reflecting the balance between fluvial advection and vertical stratification. Reduction of the vertical mixing led to a more clearly defined and extended stratification. Bottom friction is the main mixing mechanism, but as stratification is strengthened along the river mouth, the vertical mixing weakens, promoting the developing of TMZ (SSC >4500 mg l−1). Our results indicate that TMZ developing is caused by the convergence of stratified layers near the nodal point and the weakening of bottom turbulence by stratification. Although the TMZ exhibited a strong seasonal variation in its magnitude and extent, it usually formed in the convergence front demarcated by saltwater intrusion. Streamflow is the major influence on the TMZ formation since it allows the development of stratified conditions, which weaken turbulent mixing, and thus promotes particulate matter settling. The Magdalena River estuary can be classified as turbid (4000 mg l−1 10,000 mg l−1). These values are of the same order as the SSC reported for the Yangtze and Yellow estuaries, classified as having the largest SSC values of the world.

Limited range of emerging delivery vehicles, specifically unmanned aerial systems, creates gaps in last mile retail distribution networks and excludes significant numbers of potential customers. Implementing a two-stage distribution process is one approach that eliminates this deficit. However, dispatchers must load the vehicles hauling orders in the initial stage of the process and assign orders for delivery to transfer points in a way that ensures timely arrival while utilizing only the minimum required resources. This paper investigates the capacitated transfer point covering problem which has, until now, been unstudied. The research proposes a mathematical programming model to provide an optimal solution. The model is then applied to a realistic delivery situation using a distribution case study on the eastern coast of the U.S. and solved using a commercial optimization software. The results are analyzed, insights provided, and areas are identified for future research.

Conde-Frias, M.; Otero, L.; Restrepo, J.C.; Ortiz, J.C.; Ruiz, J., and Osorio, A.F., 2017. Swash oscillations in a microtidal dissipative beach. This paper evaluates the relationship between gravity and infragravity energy with swash oscillations as well as the model's ability to reproduce the transformation of the wave as it approaches the shore on a dissipative beach. For this purpose, numerical experiments were conducted in a dissipative beach from Cartagena, Colombia. Mean free surface measurements were carried out for 2 days in February 2014 by installing a cross-shore array of pressure sensors, as well as a Fourier analysis from these measurements to identify frequency components that characterize the waves and the energy associated with them. To implement the numerical modelling, the SWASH model (The Simulation WAves till Shore is a numerical model that resolves nonlinear shallow-water equations) was set up by using the bathymetric profile of Bocagrande Beach obtained during the field camp...

This study evaluated patterns of coastal sediment movement and deposition under different seasonal conditions (warm and cold phases of the El Ni˜no–Southern Oscillation [ENSO] and normal conditions) in Cartagena Bay, Colombia. A calibrated numerical model (MOHID modeling system) was applied to assess the spatial distribution of sediments transported by the Canal del Dique to the bay and studied the spatial distribution and major textural characteristics of 234 surface sediment samples. Currents in the Bay of Cartagena are controlled primarily by the strength and direction of the wind. Model results show major sediment deposition in the southern sector of the bay during the dry season. More homogenous spatial distribution of sediments throughout the Bay of Cartagena occurs with an increase in river inputs from the Canal del Dique. These patterns were enhanced or weakened, respectively, by cold and warm phases of the ENSO. Predominant sediments were medium size (u ¼ 5.35 6 1.2), poorly sorted (r ¼ 1.63 6 0.8), with notable asymmetry (Sk ¼ 0.052 6 0.2) and kurtosis (k ¼ 0.84 6 0.4). Sediments with lower sand content (,5%) are located along a latitudinal axis from the Canal del Dique delta to the western end of the island of Tierrabomba. CaCO3 content of the sediments is ,10%. Water and sediment flow, controlled by the Canal del Dique, has favored the transport and deposition of poorly sorted, symmetric, and mesokurtic mud in most of the Bay of Cartagena. As a result, autogenous calcareous sediments have been covered by fine terrigenous sediments that were delivered via the Canal del Dique. Thus, the channel plays a more prominent role in sediment transport and deposition in the Bay of Cartagena than thought previously.

This paper demonstrates an approach to evaluating production factors related to schedule and resources based on changes to the production process. A discrete event simulation model was developed to represent a moderately sized, low yield production facility to evaluate different production factors, such as arrival times, number of entities, resource availabilities, and work in process times, compared to the total yield at the production facility. A comparative analysis with cost was performed to determine the effect that varied resources had on the total output to determine the optimal solution to increasing and/or maintaining the production yields of those prior to the changes to the production process. The goal of approaching production based on related factors was to demonstrate the effect that redesigning a product and the related production process has on the production outputs. The results helped to successfully develop a model that is able to provide analytical data to a business case proposal for optimizing production resource allocations. Future research areas were identified for extending this work to additional applications as well as production facility types.

Bolívar, M.; Rivillas-Ospina, G.; Fuentes, W.; Guzmán, A.; Otero, L.; Ruiz, G.; Silva, R.; Mendoza, E.; Maza, M.; García, L., and Berrío, Y., 2019. Anthropic impact assessment on coastal ecosystems in the municipality of Puerto Colombia, NE Colombia. In: Silva, R.; Martínez, M.L.; Chávez, V., and Lithgow, D. (eds.), Integrating Biophysical Components in Coastal Engineering Practices. Journal of Coastal Research, Special Issue No. 92, pp. 112–120. Coconut Creek (Florida), ISSN 0749-0208. Human activity, mainly associated with the construction of coastal infrastructure, has caused ecological degradation to the coastal zone at Puerto Colombia, Colombia. Severe erosion attacked the ancient sandy spit that protected the municipality from wave energy to an extent that it completely disappeared. The groins constructed thereafter, with the aim of protecting the coast, have exacerbated the sediment imbalance, affecting the Balboa coastal wetland. At present, the coastline around Puerto Colombia is highly vulnerable; the remaining ecosystems are under severe pressure, and the population is at risk. Restoring the marine and terrestrial connectivity is key to improving this situation. ©Coastal Education and Research Foundation, Inc. 2019.

This work analyzes the coastal impacts of the combined effect of extreme waves and sea level extremes, including surges and projected mean sea level rise in Bocagrande, Cartagena (Colombia). Extreme waves are assessed from a wave reanalysis that are propagated from deep waters to the beach considering the hydrodynamic processes and taking into account the interaction between waves and the coastal elevation within the study area. First, we consider present sea level, storm surges and waves affecting the area. Next, we analyze the effect of sea level rise according to a moderate (RCP4.5) climate change scenario for the 21st century (years 2025, 2050, 2075, and 2100). The most pessimistic scenario (year 2100) yields a percentage of flooded area of 97.2%, thus revealing the major threat that represents sea level rise for coastal areas in the Caribbean Sea., This work was supported by COLCIENCIAS (Departamento Administrativo de Ciencia, Tecnología e Innovación) through of convocatoria 727 and from the Spanish Government MICINN/FEDER through projects MUSA (CTM2015-66225-C2-2-P) and MOCCA (RTI2018-093941-B-C31). The authors acknowledge the financial support from the Spanish Government MICINN/FEDER through projects MUSA (CTM2015-66225-C2-2-P) and MOCCA (RTI2018-093941-B-C31). AO-R is supported by a grant from Colombian COLCIENCIAS (Convocatoria 727). JS thanks the financial support by the Netherlands Scientific Research Foundation (NWO) through the VIDI grant number 864.13.011 awarded to C. A. Katsman. IH-C acknowledges the Vicenç Mut contract funded by the Government of the Balearic Island. This work was partially performed while AO was a visiting scientist at the Earth, Environmental and Planetary Sciences Department at Brown University through a Ministerio de Ciencia, Innovación y Universidades fellowship (PRX18/00218).

Restrepo, J.C.; Schrottke, K.; Traini, C.; Ortiz, J.C.; Orejarena, A.; Otero, L.; Higgins, A., and Marriaga, L., 2016. Sediment transport and geomorphological change in a high-discharge tropical delta (Magdalena River, Colombia): Insights from a period of intense change and human intervention (1990–2010). There is strong indication that environmental changes and human interventions have affected freshwater discharge and sediment flux in the Magdalena River (northwestern South America) within the period from 1990 to 2010. Thus, stream flow, suspended sediment load (SSL), and riverbed dynamics were analysed in this study for estimating changes in the suspended sediment transport regime as well as of erosional/depositional patterns in different zones of the delta. It can be shown that stream flow increased at a higher rate than suspended sediment transport, promoting changes in the sediment transport regime. Erosion appeared at the mouth/frontal bar and the outlet zones and modified the sedimentary ...

The Magdalena River has the greatest length (1612 km) and drainage area (257400 km 2 ) of any river system in Colombia, and its watershed occupies 24% of the Colombian territory. The river originates in the Andean Cordillera at an elevation of 3685 m and flows to its mouth in the Caribbean Sea (Bocas de Cenizas). For this study, information on daily flow discharge ( Q ), suspended sediment transport ( Q s ) and mean suspended sediment concentration ( C s ) from 1990–2010 for the Magdalena River, based on measurements at the Calamar gauging station (the closest station to the mouth), was provided by the Institute of Hydrology, Meteorology and Environmental Studies of Colombia (IDEAM in Spanish). The mean annual suspended sediment load was 169±73×10 6 t yr −1 for the period from 1990–1999 and 145±47×10 6 t yr −1 for the period from 2000–2010, amounting to a 14% reduction in the suspended sediment transport rate between the two periods. The sediment rating curve ( Q s =aQ b ) was also evaluated for these two periods (1990–1999 and 2000–2010). An analysis of the regression coefficients ( a , b ) for the two periods showed a decrease in b and an increase in a after 1999, which coincides with a decrease in suspended sediment transport. This change might be attributed to natural factors, including the accumulation of sediment in the Depresion Momposina and variation in hydro-climatological patterns in the Caribbean region. Decreases in effective transport were confirmed using three methods (Log-Normal and Normal distribution, and magnitude-frequency histogram) for the study period.