Analysis of road traffic crashes in the State of Qatar

Road traffic crashes (RTCs) are globally acknowledged as increasing threat to society, because they can affect many lives when they result in severe injury or fatality. In the State of Qatar RTCs are getting more awareness and attention, aiming to improve the traffic safety in the country. This study is an exploratory research providing different analyses of the crash data for seven consecutive years, ranging from 2010 to 2016, which is obtained from the Traffic Department in the Ministry of Interior for the State of Qatar. The objectives aim to evaluate the trend of RTC rate over time and create understanding of the influencing factors related to RTC frequency. Time series analyses show an increasing trend of RTCs leading to severe injury and a slight decreasing trend for fatal RTCs. Secondly, different RTC severity levels are related to diverse RTC causes. Furthermore, the results revealed that crashes with severe injuries or fatality for drivers as well as pedestrians are found to be significantly affected by seasonal weather variations, with the highest vulnerability in winter and autumn season. This study therefore suggests the implementation of strategies to prioritize the traffic safety of road users during the crash-prone winter and autumn seasons. - 2019, - 2019 Informa UK Limited, trading as Taylor & Francis Group.This publication was made possible by the NPRP award [NPRP 9-360-2-150] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Exploiting genetic variation from unadapted germplasm—An example from improvement of sorghum in Ethiopia

Societal Impact Statement The productivity of sorghum in Ethiopia has been largely limited by rain-fed condi- tions because farmers tend to use local drought-tolerant but low-yielding landraces, as high-yielding and late-maturing landrace cultivars risk failure due to drought. Addressing such issues often requires a far-reaching approach to identify and incorporate new traits into a gene pool, followed by a period of selection to re-establish an overall adaptive phenotype. The sorghum backcross nested association mapping (BC-NAM) population developed in this study increases the genetic diversity available in Ethiopian elite adapted sorghum germplasm, providing new scope to improve food security in a region known for periodic devastating droughts. Summary • As the center of diversity for sorghum, Sorghum bicolor (L.) Moench, elite cultivars selected in Ethiopia are of central importance to sub-Saharan food security. Despite being presumably well adapted to their center of diversity, elite Ethiopian sorghums nonetheless experience constraints to productivity, for example, associ- ated with shifting rainfall patterns associated with climate change. • A sorghum backcross nested association mapping (BC-NAM) population developed by crossing 13 diverse lines preidentified to have various drought resilience mechanisms with an Ethiopian elite cultivar, Teshale, was tested under three rainfed environments in Ethiopia. • Twenty-seven, 15, and 15 quantitative trait loci (QTLs) with predominantly small additive effects were identified for days to flowering, days to maturity, and plant height, respectively. Many associations detected in this study corresponded closely to known or candidate genes or previously mapped QTLs, supporting their validity. • The expectation that genotypes such as Teshale from the center of diversity tend to have a history of strong balancing selection, with novel variations more likely to persist in small marginal populations, was strongly supported in that for these three traits, nearly equal numbers of alleles from the donor lines conferred increases and decreases in phenotype relative to the Teshale allele. Such rich variation provides a foundation for selection to arrive at a new “adaptive peak,” exemplifying the nature of efforts that may be necessary to adapt many crops to new climate extremes

Least concern to endangered: applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee

Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO) and population numbers for wild Arabica coffee. Under climate change alone, our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present-day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature’s Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (Non-threatened) when applying the IUCN Red List criteri

Efficacy of FRCM systems in flexural strengthening of RC T-beams

This paper presents an experimental study on the flexural behaviour of RC beams strengthened with fabric reinforced cementitious matrix (FRCM) system. Eight T-shaped RC beams with two different flexural reinforcement ratios (?s = 0.40% and ?s = 1.02%) were constructed and tested as simply supported under monotonic three-point loading. Two beams were kept unstrengthen to act as references while the remaining six beams were strengthened with different types and geometric schemes of FRCM system. Three different test parameters have been considered: (a) FRCM type (carbon and glass), (b) FRCM strengthening scheme (side bonded versus U-shaped scheme), and (c) internal flexural reinforcement ratio. The strengthening system increased the ultimate load carrying capacity by 16.52 - 46.73% in carbon FRCM strengthened beams and 4.84 - 29.41% for glass fRcM strengthened beams relative to the reference beams. The strengthening performance of FRCM system decreased with an increase in the amount of internal flexural reinforcement. Moreover, U-shaped strengthening scheme performed better than the sided bonded counterparts in terms of the gain in the ultimate load and failure modes. � 2018 Institute of Physics Publishing. All rights reserved.This paper was made possible by NPRP grant # NPRP 7-1720-2-641 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.Scopu

Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites

The application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened with inorganic composites has been a challenging problem due to its complex failure mechanism and the interaction between the internal and external shear reinforcements. Besides, the predictive capabilities of the existing models are not satisfactory. Thus, this research proposed machine learning (ML) based models for predicting the shear capacity of RC beams strengthened in shear with inorganic composites, for the first time, considering all important variables. The results of the analyses evidenced that the proposed ML models can be successfully used to predict the shear capacity of shear-critical RC beams strengthened with inorganic composites. Among the ML models examined herein, the extreme gradient boosting (xgBoost) model showed the highest prediction capability. The comparison among the predictions of the proposed xgBoost and existing models evidenced that the efficacy of the xgBoost model is superior to the existing models in terms of accuracy, safety, and economic aspects. Finally, reliability analysis is performed to calibrate the resistance reduction factors in order to attain target reliability indices of 3.5 and 4.0 for the proposed model. 2022 The AuthorsThis paper was made possible by NPRP Grant # NPRP 13S-0209-200311 from the Qatar National Research Fund (a member of Qatar Foundation) and financial support of the Natural Sciences and Engineering Research Council (NSERC) of Canada. Open Access funding provided by the Qatar National Library. The findings achieved herein are solely the responsibility of the authors.Scopu

Plastic hinge length of rectangular RC columns using ensemble machine learning model

It is critical to properly define the plastic hinge region (the region that is exposed to maximum plastic deformation) of reinforced concrete (RC) columns to assess their performances in terms of ductility and energy dissipation capacity, implement retrofitting techniques, and control damages under lateral loads. The plastic hinge length (PHL) is used to define the extent of damages/plastic deformation in a structural element. However, accurate determination of the plastic hinge length remains a challenge. This study leveraged the power of ensemble machine learning algorithms by combining the performances of different base models and proposed a robust ensemble learning model to predict the PHL. The prediction of the proposed model is compared with those of existing empirical models and guideline equations for the PHL. The proposed model outperformed the predictions of all models and resulted in a superior prediction with a coefficient of determination (R2) between the experimental and predicted values for PHL of 98%. Furthermore, the SHapley Additive exPlanations (SHAP) approach is used to explain the predictions of the model and highlight the most significant factors that influence the PHL of rectangular RC columns. 2021 Elsevier LtdFinancial contributions of the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants are gratefully acknowledged.Scopu

Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM

This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. Beam geometry, internal steel reinforcement area, FRCM reinforcement area, and mechanical characteristics of concrete, steel, and FRCM are the main input parameters included in the database. Among the studied ML models, the xgBoost model is the most accurate model with the highest coefficient of determination (R2=99.3%) and least root mean square (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). A comparative study of the performance of the proposed and existing analytical models revealed the superior predictive capability and robustness of the proposed model. The predicted flexural and load capacities of the beams based on the existing analytical models are highly scattered and either over-conservative or unsafe. A unified SHapley Additive exPlanations approach is employed to explain the output of the best ML model and identify the most significant input features and interactions that influence the capacity of FRCM-strengthened RC beams in flexure. Furthermore, a reliability analysis is performed to calibrate the resistance reduction factor (ϕ) to achieve a specified target reliability index (βT=3.5).This paper was made possible by NPRP grant # NPRP 13S-0209-200311 from the Qatar National Research Fund (a member of Qatar Foundation) and financial support of the Natural Sciences and Engineering Research Council (NSERC) of Canada . Open Access funding provided by the Qatar National Library. The findings achieved herein are solely the responsibility of the authors.Scopu