Iranian Journal of Science and Technology-Transactions of Civil Engine

Papers
(The H4-Index of Iranian Journal of Science and Technology-Transactions of Civil Engine is 18. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)
ArticleCitations
Comparison of Existing Empirical Equations for Blast Peak Positive Overpressure from Spherical Free Air and Hemispherical Surface Bursts59
Concrete Road Crack Detection Using Deep Learning-Based Faster R-CNN Method48
Estimation of Actual Evapotranspiration Using Soil Moisture Balance and Remote Sensing41
Investigation on the Effect of Seawater Condition, Sulphate Attack, Acid Attack, Freeze–Thaw Condition, and Wetting–Drying on the Geopolymer Concrete41
Effect of SiO2 and ZnO Nano-Composites on Mechanical and Chemical Properties of Modified Concrete39
Experimental Study on Hardened Mechanical and Durability Properties of Industrial Ash Bricks31
Development of a New Stacking Model to Evaluate the Strength Parameters of Concrete Samples in Laboratory30
Setting Time, Workability and Strength Properties of Alkali Activated Fly Ash and Slag Based Geopolymer Concrete Activated with High Silica Modulus Water Glass30
Experimental and Modeling Investigation of Physicomechanical Properties and Firing Resistivity of Cement Pastes Incorporation of Micro-Date Seed Waste27
Finite Difference Modelings of Groundwater Flow for Constructing Artificial Recharge Structures27
Study on Mechanical Properties of Displacement-Amplified Mild Steel Bar Joint Damper26
Streamflow Prediction Based on Artificial Intelligence Techniques24
Fiber Type and Curing Environment Effects on the Mechanical Performance of UHPFRC Containing Zeolite23
A Novel Hybrid Algorithms for Groundwater Level Prediction21
Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms21
Comparative Study on the Machine Learning and Regression-Based Approaches to Predict the Hydraulic Jump Sequent Depth Ratio20
Vision-Based Crack Detection of Asphalt Pavement Using Deep Convolutional Neural Network19
Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco18
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