Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data.

While the cost of road traffic fatalities in the U.S. surpasses $240 billion a year, the availability of high-resolution datasets allows meticulous investigation of the contributing factors to crash severity. In this paper, the dataset for Trucks Involved in Fatal Accidents in 2010 (TIFA 2010) is utilized to classify the truck-involved crash severity where there exist different issues including missing values, imbalanced classes, and high dimensionality. First, a decision tree-based algorithm, the Synthetic Minority Oversampling Technique (SMOTE), and the Random Forest (RF) feature importance approach are employed for missing value imputation, minority class oversampling, and dimensionality reduction, respectively. Afterward, a variety of classification algorithms, including RF, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Gradient-Boosted Decision Trees (GBDT), and Support Vector Machine (SVM) are developed to reveal the influence of the introduced data preprocessing framework on the output quality of ML classifiers. The results show that the GBDT model outperforms all the other competing algorithms for the non-preprocessed crash data based on the G-mean performance measure, but the RF makes the most accurate prediction for the treated dataset. This finding indicates that after the feature selection is conducted to alleviate the computational cost of the machine learning algorithms, bagging (bootstrap aggregating) of decision trees in RF leads to a better model rather than boosting them via GBDT. Besides, the adopted feature importance approach decreases the overall accuracy by only up to 5% in most of the estimated models. Moreover, the worst class recall value of the RF algorithm without prior oversampling is only 34.4% compared to the corresponding value of 90.3% in the up-sampled model which validates the proposed multi-step preprocessing scheme. This study also identifies the temporal and spatial (roadway) attributes, as well as crash characteristics, and Emergency Medical Service (EMS) as the most critical factors in truck crash severity

Numerical Modeling of Corrosion Effectson Ultimate Strength of DX Tubular Joints

Abstract This article presents the results of numerical investigation on modeling buckling behavior and ultimate strength of corroded multi-planar tubular joints. Finite element method was used in order to simulate the behavior of DX multi-planar tubular joints under axial compressive loading. Three different patterns were chosen for corrosion modeling. Also the effects of corrosion-related parameters such as age and depth of corrosion were evaluated. The first corrosion pattern is based on uniform reduction of wall thickness over a portion of tube length while the second pattern represents a sinusoidal reduction of thickness. The third pattern of corrosion uses average thickness and standard deviation as main parameters for defining a random corroded region. A linear criterion for predicting corrosion wastage has been used for the first and the second patterns, whereas predictions of the third pattern are determined by a nonlinear method. The results indicate differences in the ultimate strength concluded from different patterns. It was found that conventional methods are conservative in evaluating the strength of corroded tubular joints of jacket platforms. Amongst 3 methods used for modeling corrosion, the third and the second pattern had similar results. It was also shown that corrosion is ineffective in braces and increasing the number of waves for the second pattern will result in increase of joint strength. The optimum sizes of elements were defined by implementing an analysis of model sensitivity toward element size

Correlation-augmented Naïve Bayes (CAN) Algorithm: A Novel Bayesian Method Adjusted for Direct Marketing

Direct marketing identifies customers who buy, more probable, a specific product to reduce the cost and increase the response rate of a marketing campaign. The advancement of technology in the current era makes the data collection process easy. Hence, a large number of customer data can be stored in companies where they can be employed to solve the direct marketing problem. In this paper, a novel Bayesian method titled correlation-augment naïve Bayes (CAN) is proposed to improve the conventional naïve Bayes (NB) classifier. The performance of the proposed method in terms of the response rate is evaluated and compared to several well-known Bayesian networks and other well-known classifiers based on seven real-world datasets from different areas with different characteristics. The experimental results show that the proposed CAN method has a much better performance compared to the other investigated methods for direct marketing in almost all cases

Performance Evaluation of Omni-Channel Distribution Network Configurations considering Green and Transparent Criteria under Uncertainty

Satisfying customer demand is one of the growing concerns of supply chain managers. On the other hand, the development of internet communications has increased online demand. In addition, the COVID-19 pandemic has increased the demand for online shopping. One of the useful concepts that help to address this concern is the omni-channel strategy, which integrates online and traditional channels with the aim of improving customer service level. For this purpose, this paper proposes an algorithm for evaluating Omni-channel Distribution Network Configurations (OCDNCs). The algorithm applies an extended Data Envelopment Analysis (DEA) model to evaluate OCDNCs based on cost, service, transparency, and environmental criteria; and then, forms a consensus on the evaluation results generated according to different criteria by utilizing an uncertain optimization model. To the best of our knowledge, this is the first attempt in which such an algorithm has been employed to take into account the mentioned criteria in a model to evaluate OCDNCs. The application of the proposed models was investigated in a case study in relation to the Indian retail industry. The results show that the configuration with the most connections among its members was the most stable, robust, and efficient

Performance Evaluation of Omni-Channel Distribution Network Configurations considering Green and Transparent Criteria under Uncertainty

Satisfying customer demand is one of the growing concerns of supply chain managers. On the other hand, the development of internet communications has increased online demand. In addition, the COVID-19 pandemic has increased the demand for online shopping. One of the useful concepts that help to address this concern is the omni-channel strategy, which integrates online and traditional channels with the aim of improving customer service level. For this purpose, this paper proposes an algorithm for evaluating Omni-channel Distribution Network Configurations (OCDNCs). The algorithm applies an extended Data Envelopment Analysis (DEA) model to evaluate OCDNCs based on cost, service, transparency, and environmental criteria; and then, forms a consensus on the evaluation results generated according to different criteria by utilizing an uncertain optimization model. To the best of our knowledge, this is the first attempt in which such an algorithm has been employed to take into account the mentioned criteria in a model to evaluate OCDNCs. The application of the proposed models was investigated in a case study in relation to the Indian retail industry. The results show that the configuration with the most connections among its members was the most stable, robust, and efficient