Productos ANID

Since the 1985 M 8.0 central Chile earthquake, national strong-motion seismic networks have recorded ten megathrust earthquakes with magnitudes greater than M 7.5 at the convergent margin, defined by the contact between the Nazca and South American plates. The analysis of these earthquake records have led to improved hazard analyses and design codes for conventional and seismically protected structures. Although strong-motion baseline correction is required for a meaningful interpretation of these records, correction methods have not been applied consistently in time. The inconsistencies between correction methods have been neglected in the practical use of these records in practice. Consequently, this work aims to provide a new strong-motion database for researchers and engineers, which has been processed by traceable and consistent data processing techniques. The record database comes from three uncorrected strong motion Chilean databases. All the records are corrected using a four-step novel methodology, which detects the P-wave arrival and introduces a baseline correction based on the reversible-jump Markov chain Monte Carlo method. The resulting strong motion database has more than 2000 events from 1985 to the date, and it is available to download at the Simulation Based Earthquake Risk and Resilience of Interdependent Systems and Networks (SIBER-RISK) project website.

According to the United Nations, in order to avoid some of the worst outcomes of climate change, the world must achieve net zero carbon emissions by 2050 at the very latest. That is the aim of the Race to Zero Campaign. Since 2019, more than 10,000 organizations around the world have embraced Race to Zero, which has been able to successfully create a relevant, global, and diverse cross-sector partnership, tearing down the barriers that have emerged from uncertainty. Building on three key cross-partnership elements (strategic, institutional, and learning), combined with the uncertainties faced by the partners, we describe the resources and activities that have made that possible.

Wildfires are an important disturbance affecting catchments’ soil and hydrological processes within. Wildfires are predicted to increase in both frequency and severity under climate change. Here, we present measurements of tritium (3H) in surface water of three streams before and after the ‘las Máquinas’ megafire of January 2017 in central Chile and streamflow metrics. Mean transit times (MTTs) of water were calculated in three coastal catchments with the Mediterranean climate type, covered by native forest, a mixture of native forest and Pinus radiata D. Don, and P. radiata. Lumped parameter models (LPMs) were used to obtain MTTs. Tritium activities from 2012 to 2018 ranged from 0.597 to 0.927 Tritium Units (TU), with the lowest TU activity in 2018. These 3H concentrations indicated water ages from 5 to 30 years. Following the fire, peak flows and baseflow have increased in two catchments but decreased in the third. Even though we have seen changes in the hydrological responses within the three catchments, pre- and post-fire MTT values were not significantly different. Therefore, there is no conclusive evidence of hydrological changes at the groundwater level due to wildfire at this early stage. However, since the MTT ranges from 5 to 30 years, it is likely that more time is required for the changes in the hydrograph to be clearly reflected in the tritium signal even though there are noticeable changes in streamflow metrics such as runoff and baseflow. Within the following years from this study, a sampling schedule to continue to investigate both the long-term drought and the effect of wildfire on these catchments will be maintained.

Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of 9.28%.

Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway. In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate negative externalities, such as congestion and pollution.

An experimental method to measure forces of small magnitude with a strain gauge as a force sensor in the fluid–wall interaction of open water channels is presented. Six uniaxial strain gauges were employed for this purpose, which were embedded across the entire sensing area and subjected to pure bending, employing two-point bending tests. Sixteen two-point bending tests were performed to determine the existence of a direct relationship between the load and the instrument signal. Furthermore, a regression analysis was used to estimate the parameters of the model. A data acquisition system was developed to register the behavior of the strain gauge relative to the lateral displacement induced by the loading nose of the universal testing machine. The results showed a significant linear relationship between the load and the instrumental signal, provided that the strain gauge was embedded between 30% and 45% of the central axis in the sensing area of the sensor (R2 > 0.99). Thus, the proposed sensor can be employed to measure forces of small magnitude. Additionally, the linear relationship between the load and the instrumental signal can be used as a calibration equation, provided that the strain gauge is embedded close to the central axis of the sensing area.

A great deal of research has examined the efficacy of variable message signs (VMS) to induce driver behavior changes, improve safety conditions, and decongest the traffic network. However, there is little literature regarding the most effective ways to display this information on VMS. Furthermore, none of the previous contributions have concentrated on analyzing what impact flashing VMS have on drivers by using real traffic data. This article seeks to bridge this gap, analyzing the effect of incorporating intermittent light stimulation to messages on drivers’ behavior on a Chilean highway, using vehicle-by-vehicle data obtained in a non-intrusive way. In order to do so, an experiment was carried out to measure the responses of drivers when faced with two types of messages: (1) those intended to induce a speed reduction and (2) those aimed at generating lane changes. From the statistical models we obtained several insights. Our results show that flashing messages may increase the effectiveness of VMS depending on environmental and traffic conditions. In particular, for speed moderation messages, we found 12 significant effects, showing, for example, that a flashing message is most effective in the hours of darkness, with low congestion, small spacing, and low average speeds. Additionally, it has a more significant impact on experienced drivers. On the other hand, for lane change messages, we found five significant effects, showing that flashing messaging reduces its effectiveness in situations where a high cognitive load is required, such as in high flow and high average speeds. No particular effects were identified in either case for specific vehicle types.