Impact Factor, Scope & Trends of Journal of Computing in Civil Engineering

Introduction

Over recent years, many researchers have been searching for Journal of Computing in Civil Engineering impact factor 2024, wanting to know how high its h-index is and how long the peer review time and acceptance rate might be. They also ask how to publish in Journal of Computing in Civil Engineering (ASCE) and what are its scope and topics. At the same time, popular technical interests include machine learning applications in civil infrastructure monitoring, simulation and optimization methods in civil engineering computing, digital twins use in civil engineering projects, building information modeling (BIM) research, and recent advances in artificial intelligence in civil engineering journal articles. This article addresses all these questions: we explore its impact factor, scope, topics, recent research trends like AI, BIM, digital twins, optimization methods, and what publishing in this journal involves.

Modern civil engineering journal with architectural drawings and laptop showing research and innovation.


Contents

  1. Introduction

  2. What is the Journal of Computing in Civil Engineering?

  3. Impact Factor 2024 & Other Metrics

  4. Scope & Topics Covered

  5. Recent Advances: Artificial Intelligence in Civil Engineering

  6. Digital Twins and Their Role in Projects

  7. Building Information Modeling (BIM) Research Trends

  8. Machine Learning for Infrastructure Monitoring

  9. Simulation & Optimization Methods in Civil Computing

  10. How to Publish: Submission, Review Time & Acceptance

  11. Key Challenges & Future Directions

  12. Conclusions

2. What is the Journal of Computing in Civil Engineering?

The Journal of Computing in Civil Engineering is a peer-reviewed scientific journal published by the American Society of Civil Engineers (ASCE). It has been in publication since 1987, issued bimonthly. Its aim is to serve researchers, practitioners, and students by presenting advances in computing as applied to civil engineering. Typical topics include: software tools, simulation, optimization, data modelling, imaging, expert systems, robotics, remote sensing, database management, and computing resource management.

The journal is indexed in major databases such as SCIE, Scopus, etc., making its publications widely visible in the scientific community.

3. Impact Factor 2024 & Other Metrics

  • The impact factor for Journal of Computing in Civil Engineering in 2024 is approximately 6.04.

  • Its h-index is around 107.

  • According to SCImago, the journal’s SJR (SCImago Journal Rank) is about 1.244, placing it in Q1 in its subject categories (Civil & Structural Engineering; Computer Science Applications). 

  • Publication frequency is bimonthly.

These metrics reflect a strong reputation in both civil engineering and computing domains. The rising impact factor suggests growing relevance especially in areas like AI, machine learning, BIM, and digital twins.

4. Scope & Topics Covered

The journal focuses on the intersection of computational methods and civil engineering practice. Key topics include:

  • Software, algorithms, and simulation: Development of new modeling, simulation techniques, optimization methods.

  • Artificial Intelligence (AI), Machine Learning (ML), Computer Vision: Use in infrastructure monitoring, crack detection, data analytics.

  • Building Information Modeling (BIM), Information Models, Industry Foundation Classes (IFC): For visualization, management, design, and construction phases.

  • Digital Twins: Representing structures in virtual/real couples, for monitoring, maintenance, decision support.

  • Remote sensing, Robotics, Point Cloud Data: Capturing, analyzing data from UAVs, LiDAR, etc.

  • Optimization and Decision-Support Systems: Tools to assist planning, structural design, resource allocation.

  • Infrastructure Monitoring & Structural Health Monitoring (SHM): Data-driven monitoring, sensors, real-time diagnosis.

Thus, researchers whose work touches any of these areas are within scope.

5. Recent Advances: Artificial Intelligence in Civil Engineering

Artificial intelligence is growing fast in relevance in Journal of Computing in Civil Engineering. Researchers use AI and machine learning for tasks like:

  • Predictive modelling: For example, predicting asphalt concrete overlay performance in hot, humid climates using ML tools.

  • Damage detection: Crack detection, semantic segmentation in images from bridges or roads, using computer vision.

  • Risk analysis: Using images or video data for analyzing safety hazards in construction sites.

With increasing compute power and availability of data (e.g., UAV/drone imagery, sensors), AI methods are more feasible. The journal offers recent papers where novel AI models (deep learning, encoder-decoder architectures, contrastive learning, etc.) are applied to infrastructure, safety, facility surface quality, anomaly detection.

Challenges like generalizability, data quality, real-time processing, interpretability remain important. But AI is clearly one of the major trending topics.

6. Digital Twins and Their Role in Projects

Digital twins refer to virtual replicas of physical civil engineering systems (bridges, buildings, road networks, etc.) that are continuously updated via data so one can monitor, simulate, predict performance over time. Relevant recent work:

  • Frameworks coupling sensor data with deep learning and probabilistic models for real-time structural health diagnostics.

  • Studies showing how digital twins integrate with Building Information Modeling (BIM) tools, enhance maintenance planning, lifecycle cost reduction.

  • Use in operations & maintenance phases, but fewer works yet in construction phase, which remains transient and complex.

Digital twins are attractive because they allow predictive maintenance, better resource allocation, risk mitigation. But setting them up has costs: sensor deployment, data integration, model fidelity, uncertainty quantification. The journal is increasingly publishing research to address these barriers.

7. Building Information Modeling (BIM) Research Trends

BIM remains a central theme:

  • Use of BIM for tunnel information modeling, visualization and simulation in large underground construction.

  • Integration of BIM with augmented reality, point cloud data for facility management and maintenance.

  • Use of Industry Foundation Classes (IFC) for data interoperability.

Trends include improving the semantic richness of BIM models, combining BIM with remote sensing or robotics, and using BIM data for digital twin frameworks. Research also addresses challenges in data standardization, model updating, handling large data, ensuring model accuracy.

8. Machine Learning for Infrastructure Monitoring

Infrastructure monitoring has benefited a lot from machine learning:

  • Structural health monitoring (SHM): Using sensor networks, time-series data, anomaly detection, functional data analysis for bridges, buildings.

  • Image / video based monitoring: Using UAVs, remote imagery for flood estimation, crack detection, pavement condition surveys.

  • Predictive maintenance: ML to predict infrastructure deterioration, service life, schedule maintenance optimally.

Key advantages: ability to process large unstructured data, early detection, automated monitoring. Limitations include need for labeled data, dealing with environmental noise, real-time processing, generalizing models across different infrastructure types. The journal publishes recent works tackling these.

9. Simulation & Optimization Methods in Civil Computing

Simulation and optimization are foundational in this journal’s content. Some illustrative areas:

  • Numerical simulations for structural behaviour, fluid dynamics, ground motions.

  • Optimization algorithms: Hybrid evolutionary algorithms, multi-objective optimizations, decision support systems, optimization under uncertainty.

  • Real-case studies: Using simulation + optimization for traffic, water distribution networks, design under load, etc.

These methods help in design, cost-efficiency, resilience, sustainability. Important aspects are ensuring model validity, computational efficiency, balancing between accuracy and speed, and integrating optimization with data‐driven and AI methods.

10. How to Publish: Submission, Review Time & Acceptance

If you're considering publishing in Journal of Computing in Civil Engineering, here are important details:

  • How to submit: Manuscripts normally submitted via ASCE’s online submission system; follow author guidelines regarding formatting, references, scope, etc.

  • Review Time: Review process tends to take about 6-12 weeks on average.

  • Acceptance Rate: Not publicly fixed (varies by year and field), but it is a competitive journal especially for novel, data-rich, well-validated work.

Tips for getting accepted:

  • Ensure your research fits the topics such as AI, BIM, digital twins, ML, simulation, optimization.

  • Provide strong validation (real-world data, comparison with existing models).

  • Emphasize novelty and clarity, good writing.

  • Use standard data formats, share datasets if possible.

11. Key Challenges & Future Directions

Some of the major challenges and promising future directions are:

  • Data availability & quality: Data for infrastructure can be sparse, noisy, or proprietary; improving open datasets is essential.

  • Model interpretability & robustness: Especially for AI/ML models, ensuring that results are explainable and trustworthy.

  • Real-time processing & scalability: Handling large datasets, sensor streams, point clouds, video in near-real time.

  • Integration across phases: From planning / design (with BIM) through construction, operations, maintenance (digital twins). Bridging gaps.

  • Standardization: Data models, interoperability (IFC, BIM standards), benchmark datasets.

  • Sustainability & resilience: Considering climate change, extreme events, adaptability of infrastructure.

Future work likely to focus more on hybrid models (combining physics-based + data-driven), augmented reality/virtual reality, edge computing, IoT, computationally efficient AI, perhaps quantum-inspired methods.

12. Conclusions

  1. The Journal of Computing in Civil Engineering (ASCE) remains a high-impact, Q1 journal with rising impact factor (~6.04 in 2024) and a strong h-index (~107).

  2. Its scope includes AI, ML, simulation, BIM, digital twins, remote sensing, optimization etc.

  3. Recent research trends show growing work in artificial intelligence, especially image/video-based monitoring, predictive modelling, and safety.

  4. Digital twins are becoming more prominent, especially for maintenance and operations phases.

  5. BIM research remains critical for design, visualization, management & interoperability.

  6. Infrastructure monitoring leveraging ML and sensor data is advancing, but faces challenges in data & generalizability.

  7. Simulation & optimization methods continue to be core tools; integrating them with AI/ML is a promising direction.

  8. Publishing in this journal requires novelty, good validation, matching topics, clarity, and adherence to ASCE guidelines.

  9. Review times are moderate (≈ 6-12 weeks), and acceptance is competitive.

  10. Future directions include real‐time systems, sustainable and resilient infrastructure, standardization, hybrid modelling, and advanced data integration.

FAQs

  1. What is the current impact factor for Journal of Computing in Civil Engineering?
    The impact factor for 2024 is around 6.04.

  2. What topics does the journal accept?
    It accepts work in areas like artificial intelligence, machine learning, simulation, optimization, digital twins, building information modeling (BIM), remote sensing, structural health monitoring, etc.

  3. How long does peer review take?
    Review process typically takes about 6-12 weeks.

  4. Is the journal indexed, and what is its h-index?
    Yes, it is well indexed (SCIE, Scopus, etc.), and has an h-index of approximately 107.

  5. What are the trending research areas right now in this journal?
    Trending areas include digital twins, AI/ML applications (especially for infrastructure monitoring), BIM and interoperability, optimization methods, simulation under uncertainty, and remote sensing/image-based diagnostics.

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