Sharif Digital Repository

Unanticipated failure of reinforced concrete structures due to corrosion of steel rebar embedded in concrete causes to increase the demand for finding methods to forecast the service life of concrete structures. In this field, the success of machine learning-based methods leads to the use of multi-gene genetic programming (MGGP) method for classifying the degree of corrosion destruction of steel in reinforced concrete in this paper. Despite the common application of MGGP that is the symbolic regression, in this research, MGGP was adapted to use in classification tasks. Accordingly, a large field database has been collected from different regions in the Persian Gulf for modeling of MGGP and... 

COVID-19, as the most significant epidemic of the century, infected 467 million people and took the lives of more than 6 million individuals as of March 19, 2022. Due to the rapid transmission of the disease and the lack of definitive treatment, countries have employed non-pharmaceutical interventions. This study aimed to investigate the effectiveness of the smart travel ban policy, which has been implemented for non-commercial vehicles in the intercity highways of Iran since November 21, 2020. The other goal was to suggest efficient COVID-19 forecasting tools and to examine the association of intercity travel patterns and COVID-19 trends in Iran. To this end, weekly confirmed cases and... 

This study investigated the mode I fracture behavior of double cantilever beam (DCB) epoxy adhesive joints with similar adherends on the both sides (i.e., aluminum-aluminum or copper-copper) at different strain rates; i.e., quasi-static (∼10−3 s−1), low (∼7 s−1) and intermediate (∼14 s−1) rates. The fracture energy of the DCB joint in Al-adhesive-Al specimens decreased (i.e., by ∼62%, p = 0.0013) with an increase in the applied strain rate from quasi-static to low values, while it remained almost unchanged with further increase of stain rate to intermediate range (p > 0.05). For Cu-adhesive-Cu cases, however, the fracture energy was found to be almost insensitive to the applied strain rate... 

Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant... 

Despite complex fluctuations, missing data, and maintenance costs of detectors, traffic volume forecasting at intersections is still a challenge. Moreover, most existing forecasting methods consider an isolated intersection instead of multiple adjacent ones. By accurately forecasting the volume of short-term traffic, a low-cost method can be provided to solve the problems of congestion, delay, and breakdown of detectors in the road transport system. This paper outlines a novel hybrid method based on deep learning to estimate short-term traffic volume at three adjacent intersections. The gated recurrent unit (GRU) and long short-term memory (LSTM) bilayer network with wavelet transform (WL)... 

This study focuses on the spectrochemical estimation of pH and titratable acidity (TA) of apples of Fuji variety at different stages of ripening. A novel approach is proposed for near-infrared (NIR) spectral analysis using hybrid machine learning methods that combine artificial neural networks (ANN) and metaheuristic algorithms. One hundred twenty samples were collected at three ripening stages and spectral data within two bands of NIR were extracted from each sample to predict the acidity properties. Alternatively, the 4 most effective wavelengths were also selected using a hybrid of ANN and the cultural algorithm. The experimental results prove that the models using spectral bands and the... 

Nanocomposites containing ZrO2 and HA have been considered in various fields due to their unique mechanical properties. The principal purpose of this paper is to select the models with the maximum accuracy for the prediction of microhardness of CHA/ZrO2 nanocomposite. For this purpose, three models, including gene expression programming (GEP), gray wolf optimization algorithm (GWOA), and least squares support vector machine (LS-SVM), were implemented to predict and optimize the microhardness of the CHA/ZrO2 nanocomposite. Finally, the results showed that the data obtained from the LS-SVM model were closer to the preliminary data than the others. According to the results, the LS-SVM could... 

Globally, the impacts of climate change can vary across different regions. This study uses a probability framework to evaluate recent historical (1976–2016) and near-future projected (until 2049) climate change across Europe using Climate Research Unit and ensemble climate model datasets (under RCPs 2.6 and 8.5). A historical assessment shows that although the east and west of the domain experienced the largest and smallest increase in temperature, changes in precipitation are not as smooth as temperature. Results indicate that the maximum changes between distributions of the variables (temperature and precipitation) mainly occur at extreme percentiles (e.g., 10% and 90%). A group analysis... 

Snakebite envenoming is an important cause of preventable death. The World Health Organization (WHO) set a goal to halve snakebite mortality by 2030. We used verbal autopsy and vital registration data to model the proportion of venomous animal deaths due to snakes by location, age, year, and sex, and applied these proportions to venomous animal contact mortality estimates from the Global Burden of Disease 2019 study. In 2019, 63,400 people (95% uncertainty interval 38,900–78,600) died globally from snakebites, which was equal to an age-standardized mortality rate (ASMR) of 0.8 deaths (0.5–1.0) per 100,000 and represents a 36% (2–49) decrease in ASMR since 1990. India had the greatest number... 

Study region: This study develops the first daily runoff forecast system for Bukan reservoir in Urmia Lake basin (ULB), Iran, a region suffering from water shortages and competing water demands. Study focus: A weather forecast downscaling model is developed for downscaling large-scale raw weather forecasts of ECMWF and NCEP to small-scale spatial resolutions. Various downscaling methods are compared, including deterministic Artificial Intelligence (AI) techniques and a Bayesian Belief Network (BBN). Downscaled precipitation and temperature forecasts are then fed into a rainfall-runoff model that accounts for daily snow and soil moisture dynamics in the sub-basins upstream of Bukan reservoir.... 

Establishing high-quality communication in construction projects is essential to securing successful collaboration and maintaining understanding among project stakeholders. Indeed, poor communication results in low productivity, poor efficiency, and substandard deliverables. While high-quality communication is recognized as contingent on the interpersonal skills of workers, the impacts of communication quality on job performance remain unknown. This study addresses this deficiency by developing a method to evaluate construction workers' communication quality. A literature review is undertaken to capture salient interpersonal skills. Leadership style, listening, team building, and clarifying... 

Despite the seismic vulnerability of non-structural Exterior Infill Walls (EIWs), their resilient design has received minimal attention. This study addresses the issue by proposing a novel framework for predicting possible damage states of EIWs. The framework benefits from an automated combination of Building Information Modeling as a visualized 3D database of the building's components and the Machine Learning classification as the prediction engine. The framework's applicability is studied in a Proof of Concept example of the exterior walls of the buildings damaged in the 2017 earthquake in Kermanshah, Iran. The Extremely Randomized Trees classifier produced the best results for predicting... 

This study evaluated the efficiency of different Digital Elevation Models (DEMs), including ALOS-12.5 m, SRTM-30 m, SRTM-90 m, and ASTER-30 m v3 when being applied for the hydraulic simulation of flood inundation areas. HEC-RAS-2D model was employed to simulate inundation extent of a 400-year flood (Mar 17, 2019, with peak discharge ~ 547.92 m3/s) along 70 km reach of low-gradient Gorganrood River, northeastern Iran. Fit percentage indicator (FI) and BIAS percentage indicator (BI) were used to evaluate the results in comparison with the remotely sensed inundated area data. The results revealed that the accuracy and capability of the ALOS and SRTM-30 m were higher in simulation of flood... 

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin–eosin-stained tissue samples, thus avoiding specialized techniques. The... 

Clay liners are widely used as porous membrane barriers to control solute transport and to prevent the leakage of leachate both in horizontal and vertical flow scenarios, such as the isolated base and ramps of sanitary landfills. Despite the primary importance of saturated hydraulic conductivity in a reliable simulation of fluid flow through clay barriers, there is no model to predict hydraulic conductivity of clayey soils permeated with saline aqueous solutions because most of the current models were developed for pure water. Therefore, the main motivation behind this study is to derive semi-empirical models for simulating the hydraulic conductivity of clayey soils in the presence of... 

Our transportation field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigation or pedestrian flow simulations, their adversarial robustness has not been carefully studied. In this paper, we introduce a socially-attended attack to assess the social understanding of prediction models in terms of collision avoidance. An attack is a small yet carefully-crafted perturbations to fail predictors. Technically, we define collision as a failure mode of the output, and propose hard- and soft-attention mechanisms to guide our attack. Thanks to our attack, we shed light on the... 

This article presented an efficient deep learning technique to predict the compressive strength of high-performance concrete (HPC). This technique combined the convolutional neural network (CNN) and genetic algorithm (GA). Six CNN architectures were considered with different hyper-parameters. GA was employed to determine the optimum number of filters in each convolutional layer of the CNN architectures. The resulted CNN architectures were then compared to each other to find the best architecture in terms of accuracy and capability of generalization. It was shown that all of the proposed CNN models are capable of predicting the HPC compressive strength with high accuracy. Finally, the best of... 

In both public and private sectors, critical technology-based tasks, such as innovation, forecasting, and road-mapping, are faced with unmanageable complexity due to the ever-expanding web of technologies which can range into thousands. This context cannot be easily handled manually or with efficient speed. However, more precise and insightful answers are expected. These answers are the fundamental challenge addressed by tech-mining. For tech-mining, discovering the associations among them is a critical task. These associations are used to form a weighted directed graph of technologies called 'association tech-graph' which is used for technology development, trend analysis, policymaking,... 

Software repositories such as GitHub host a large number of software entities. Developers collaboratively discuss, implement, use, and share these entities. Proper documentation plays an important role in successful software management and maintenance. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and finally, to document the highlight of their team’s effort. An issue report is a rich source of collaboratively-curated software knowledge, and can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it... 

We present a novel deep convolutional neural network (DCNN) model for predicting human behavior in repeated games. The model is the first deep neural network presented on repeated games that is able to be trained on games with arbitrary size of payoff matrices. Our neural network takes the players’ payoff matrices and the history of the play as input, and outputs the predicted action picked by the first player in the next round. To evaluate the model’s performance, we apply it to some experimental games played by humans and measure the rate of correctly predicted actions. The results show that our model obtains an average prediction accuracy of about 63% across all the studied games, which...