Journal of Soft Computing in Civil Engineering

Journal of Soft Computing in Civil Engineering

Journal of Soft Computing in Civil Engineering

ISO Abbreviation: J. Soft Comput. Civ. Eng. 

Journal Metrics

Acceptance Rate: 21%

 Review Speed: 140 days


Publication Start Year: 2017

Scimago Journal Rank 2024 (Q1)

SJR 2024: 0.426

CiteScoreTracker 2025: 5.6

SNIP 2024: 0.934

No. of Citations (Scopus): 2140

Scopus h-index: 23


No. of Citations (Google Scholar): 2961

Google Scholar h-index: 27

Google Scholar i10-index: 115


Issue Per Year: 4

No. of Volumes: 10

No. of Issues: 37

No. of Articles: 265

No. of Indexing Databases: 14

No. of Reviewers: 356

No. of Contributors: 684

Contributing Countries: 40


Article View: 582,360

PDF Download: 388,260

View Per Article: 2197.58

PDF Download Per Article: 1465.13


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SCImago Journal & Country Rank


CiteScore Categories

Architecture

Rank: #17/203

Engineering (miscellaneous)

Rank: #55/264

Building and Construction

Rank: #55/239

Civil and Structural Engineering

Rank: #100/407

Computer Science Applications

Rank: #286/947

Artificial Intelligence

Rank: #164/450

The Journal of Soft Computing in Civil Engineering is an international Q1 (SJR) open-access journal (online) published quarterly by Pouyan Press which was founded in 2017. The idea behind soft computing is to model the cognitive behavior of human mind. Soft computing is the foundation of conceptual intelligence in machines. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximation. Soft computing aims to surmount NP-complete problems, uses inexact methods to give useful but inexact answers to intractable problems, and also it is well suited for real world problems where ideal models are not available. Today, soft computing algorithms are becoming important classes of efficient tools for developing intelligent systems and providing solutions to complicated civil engineering problems.

The focus of this journal is on applications of soft computing methods in civil engineering. Domains of applications include structural engineering, design, diagnostics, and health monitoring, hydraulic engineering, geotechnical engineering, transportation engineering, environmental engineering, coastal and ocean engineering and construction management. Articles submitted to this journal could also be concerned about the most significant recent developments on the topics of soft computing and its application in civil engineering. The journal also provides a forum where civil engineering researchers can obtain information on relevant new developments in optimization. We encourage the submission of articles that make a genuine soft computing contribution to a challenging civil engineering problem.

Current Issue: Volume 10, Issue 3 - Serial Number 37, Summer 2026 (In Progress) 

Assessment of Micro-pile Group Capacity in Soft Clay Soils Using Closed-Form Machine Learning Models

Article ID:2023

10.22115/scce.2025.501243.2023

Tammineni Gnananandarao, Naga Dheeraj Kumar Reddy Chukka, B.A.V. Ram Kumar, Jayatheja Muktinutalapati, Vedprakash Maralapalle, Mummidivarapu Satish Kumar, CH. Ajay, P. Uma Maheswara Rao

Prediction of the Horizontal Displacement of Mechanically Stabilized Earth Wall using Soft Computing

Article ID:1959

10.22115/scce.2025.1959

Saloua Hamza, Brahim Lafifi, Mohamed Nemissi, Abdelkrim Moussaoui, Ammar Rouaiguia

Predicting the Shear Strength of Reinforced Concrete Dapped End Beams Using Machine Learning Techniques

Article ID:1957

10.22115/scce.2025.480929.1957

Ajibola Ibrahim Quadri, Agnes Onyeje Ojile, William Kehinde Kupolati, Chris Ackerman, Jacque Snyman Jacque Snyman, Julius Musyoka Ndambuki

Keywords Cloud

  • Artificial Neural Network
  • Machine Learning
  • Artificial Neural Networks
  • Compressive strength
  • optimization
  • ANN
  • Genetic Algorithm
  • Sensitivity analysis
  • Concrete
  • FRP
  • Artificial intelligence
  • prediction
  • Deep Learning
  • Shear strength
  • structural health monitoring
  • Neural Network
  • TOPSIS
  • Support Vector Machine
  • Random forest
  • Soft Computing
  • ANFIS
  • Support vector regression
  • Liquefaction
  • SVM
  • Self-Compacting Concrete
  • Random forest regression
  • Deep Neural Network
  • M5P model tree
  • Gaussian Process Regression
  • Ultimate bearing capacity
  • Fuzzy logic
  • Damage detection
  • Compression Index
  • wavelet transform
  • Reliability
  • Genetic Algorithms
  • corrosion
  • time series
  • Bearing Capacity
  • adaptive neuro-fuzzy inference system
  • pan evaporation
  • modelling
  • Particle Swarm Optimization
  • computer vision
  • earthquake
  • Layered sand
  • Ensemble Learning
  • Whale optimization algorithm (WOA)
  • Structural damage detection
  • Rice Husk Ash
  • Flexible pavements
  • Garmsar
  • MLR
  • Computer Programming
  • reliability analysis
  • Regularization
  • Artificial neural network (ANN)
  • Unconfined compressive strength
  • Remote Sensing
  • Dynamic analysis
  • PSO
  • Semnan
  • Support Vector Machines
  • Fuzzy Inference System (FIS)
  • Multi-criteria decision making
  • Particle Swarm Optimization (PSO)
  • Clustering
  • Supervised Learning
  • Dry density
  • regression analysis
  • Soft-computing techniques
  • Feature selection
  • Flexural Strength
  • tensile strength
  • GEP
  • Moment Capacity
  • Seismic Design
  • ARIMA
  • ANOVA
  • Multi-Objective Optimization
  • Digital Twin
  • plasticity index
  • Evolutionary Algorithm
  • MATLAB
  • Truss Structures
  • slope stability
  • Structural engineering
  • Pervious concrete
  • FDAHP
  • Earthquake Engineering
  • Bond strength
  • Neural Networks
  • Fly ash
  • Construction management
  • beams
  • Deep beam
  • Multiple Regression Analysis
  • Contourlet Transform
  • Feed forward backpropagation algorithm
  • runoff
  • Metaheuristic Algorithm
  • Differential evolution
  • Slump
  • Simulated Annealing
  • mean square error
  • Neuro-Fuzzy
  • MR damper
  • Multilayer Perceptron
  • site layout
  • lime
  • Gene expression programming
  • statistical
  • Uncertainty Quantification
  • Risk Management
  • reinforced concrete
  • Soft computing techniques
  • PSO algorithm
  • strengthening
  • Adaptive Neuro-Fuzzy Inference System (ANFIS)
  • image processing
  • GMDH
  • shear capacity
  • Random Forest (RF)
  • Resilient back propagation (Rprop)
  • Oil and gas plants
  • Concrete damage plasticity
  • Project scheduling
  • evaporation reduction
  • Scour, Support vector regression
  • Moment-Resisting Frames
  • Random forest for delay estimation
  • SVM RBF kernel
  • Supply chain
  • Liquefaction-potential
  • Particle Swarm Optimization Algorithm
  • Hybrid PSO-SA optimization
  • chaotic map
  • Claim types, Project management
  • NSGA-II algorithm
  • pavement distresses
  • Ensemble machine learning
  • Active seismic zone region
  • Stacking model
  • Selection Criteria
  • attributes
  • Expansive Soils
  • Precision
  • Smart Cities
  • Schmidt Hammer
  • Durability
  • SBC 301 (2007)
  • Neutrosophic interval probability
  • significant wave height
  • SPI
  • SVM Poly kernel
  • Mahabad Dam
  • Machine learning classifier
  • Marine Predators Algorithm
  • Orthogonal array
  • Rigid pavement
  • CLARANS
  • Graph Partitioning
  • Muskingum model
  • decision tree
  • Saudi Arabia
  • Stacked ensemble
  • Damage Location
  • Sewer
  • RF
  • partial dependence analysis
  • Nature-Inspired Algorithms
  • Taguchi
  • Nasik region
  • SAC method
  • SVMRBK kernel
  • Training algorithm
  • Sediment transport estimation
  • Spur Dike
  • Gradient boosting
  • Time-Cost-Quality-Environmental Trade-off Problem
  • Approximate solutions
  • Shape Optimization
  • Storey drift
  • Forecasting
  • OpenSees
  • RC Beam
  • Joint roughness coefficient (JRC)
  • Concrete Foundation
  • Seismic retrofit cost
  • Dimensional stone
  • Supervised machine learning
  • Prestressed concrete slab
  • construction project
  • RegCM4.7
  • Optimum moisture content
  • Process Parameters
  • Factor of safety
  • Seismic Retrofitting
  • Construction
  • concrete structure

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