Peer-reviewed journals 

• • Weng, Y.* and Paal, S.G. (2024). Extrapolating wind pressures on roof soffits of low-rise buildings using few-shot learning, Journal of Building Engineering, Elsevier, 82, 1 April 2024, 108110. Impact Factor: 6.4 (2023)

  • Weng, Y.*, Das, S*, and Paal, S.G. (2023). Applying Few-Shot Learning in Classifying Pedestrian Crash Typing, Transportation Research Record, Sage, 2677(8): 563-572.

  • Weng, Y.* and Paal, S.G. (2023). Extrapolation of wind pressure for low-rise buildings at different scales using few-shot learning, Journal of Wind Engineering and Industrial Aerodynamics, Elsevier. Impact Factor: 2.739 (2020)

  • Luo, H.* and Paal, S.G. (2023). A novel outlier-insensitive local support vector machine for data-driven regression, Engineering with Computers, Springer, 1-19. Impact Factor: 7.963 (2023)

  • Pak, H.*, Leach, S.*, Yoon, S.H. and Paal, S.G. (2023). A Knowledge Transfer Enhanced Ensemble Approach to Predict the Shear Capacity of Reinforced Concrete Deep Beams without Stirrups, Computer-Aided Civil and Infrastructure Engineering,38(11): 1520-1535. DOI: 10.1111/mice.12965. Impact Factor: 8.552 (2020)

  • Luo, H.* and Paal, S.G. (2023). A data-free support vector machine-based physics-driven estimator for dynamic response computation, Computer-Aided Civil and Infrastructure Engineering, Wiley, DOI: 10.1111/mice.12823. Impact Factor: 8.552 (2020)

  • Sohrabi, S., Weng, Y.*, Das, S., and Paal, S.G. (2022). Safe route-finding: A review of literature and future directions, Accident Analysis and Prevention, 177, 106816.

  • Kabir, N.*, Terzioglu, T., Hueste, M., Hurlebaus, S., Mander, J., and Paal, S.G. (2022). Experimental Investigation and Refined Load Rating of Concrete Pan Girder Bridge, Engineering Structures, Elsevier, 352, 135-154. Impact Factor: 3.548

  • Pak, H.* and Paal, S.G. (2022). Evaluation of transfer learning models for predicting the lateral strength of reinforced concrete columns, Engineering Structures, Elsevier, accepted. Impact Factor: 2.554 (2018)

  • Weng, Y.* and Paal, S.G. (2022). Machine learning-based wind pressure prediction of low-rise non-isolated buildings, Engineering Structures, Elsevier, 258 (May 2022): 114148. Impact Factor: 3.548 (2020)

  • Luo, H.* and Paal, S.G. (2022). Artificial intelligence-enhanced seismic response prediction of reinforced concrete frames, Advanced Engineering Informatics, Elsevier, 52 (April 2022): 101568. Impact Factor: 3.879 (2020)

  • Luo, H.* and Paal, S.G. (2022). A data-free support vector machine-based physics-driven estimator for dynamic response computation, Computer-Aided Civil and Infrastructure Engineering, Wiley, DOI: 10.1111/mice.12823. Impact Factor: 8.552 (2020)

  • Luo, H.* and Paal, S.G. (2021). Data-driven seismic response prediction of structural components. Earthquake Spectra, Sage. Impact Factor: 1.930 (2020)

  • Stieglitz, M.*, Terzioglu, T., Hueste, M., Hurlebaus, S., Mander, J., and Paal, S.G. (2021). Experimental and Computational Investigation of a Load-Posted Steel Beam Bridge, Engineering Structures, Elsevier, 245 (10/2021), 112963, DOI: 10.1016/j.engstruct.2021.112963. Impact Factor: 3.548 (2020)

  • Luo, H.* and Paal, S.G. (2021). Metaheuristic Least Squares Support Vector Machine-Based Lateral Strength Modeling of Reinforced Concrete Columns Subjected to Earthquake Loads, Structures, Elsevier, 33 (10/2021), 748-758, DOI: 10.1016/j.istruc.2021.04.048. Impact Factor: 1.839 (2019)

  • Leach, S.*, Xue, Y.*, Sridhar, R.*, Paal, S.G., Wang, Z., Murphy, R. (2021). The use of data augmentation for improving deep learning models in the field of building inspections or post-disaster evaluation, Journal of Performance of Constructed Facilities, ASCE, accepted 02/02/2021, DOI: 10.1061/(ASCE)CF.1943-5509.0001594. Impact Factor: 1.542 (2018)

  • Luo, H.*  and Paal, S. G. (2021). Advancing post-earthquake structural evaluations via sequential regression-based predictive mean matching for enhanced forecasting in the context of missing data. Advanced Engineering Informatics, Elsevier, DOI: 10.1016/j.aei.2020.101202. Impact Factor: 3.879 (2020) 

  • Nath, N.*, Behzadan, A., and Paal, S.G. (2020). Deep learning for site safety: Real-time detection of personal protective equipment, Automation in Construction, Elsevier. DOI: 10.1016/j.autcon.2020.103085. Impact Factor: 5.669 (2020) 

  • Luo, H.* and Paal, S.G. (2020). Reducing the effect of sample bias for small datasets with double-weighted support vector transfer regression, Computer-Aided Civil and Infrastructure Engineering, Wiley. DOI: 10.1111/mice.12617. Impact Factor: 8.552 (2020) 

  • Luo, H.* and Paal, S.G. (2019). A locally-weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments, Computer-Aided Civil and Infrastructure Engineering, Wiley. DOI: 10.1111/mice.12456. Impact Factor: 8.552 (2020) 

  • Luo, H.* and Paal, S.G. (2018). Machine Learning-Based Backbone Curve Model of Reinforced Concrete Columns Subjected to Cyclic Loading Reversals, Journal of Computing in Civil Engineering, ASCE, 32(5). DOI: 10.1061/(ASCE)CP.1943-5487.0000787. Impact Factor: 2.554 (2018) 

  • Aghababaei, M.*, Koliou, M., and Paal, S.G. (2018). Performance Assessment of Building Infrastructure Impacted by the 2017 Hurricane Harvey in the Port Aransas Region, Journal of Performance of Constructed Facilities, ASCE, 32(5). DOI: 10.1061/(ASCE)CF.1943-5509.0001215.  Impact Factor: 1.542 (2018)

  • Moser, G.*, Paal, S.G., and Smith, I.F.C. (2018). Leak detection of water supply networks using error-domain model falsification, Journal of Computing in Civil Engineering, ASCE, 32 (2) (March 2018), 10.1061/(ASCE)CP.1943-5487.0000729. Impact Factor: 2.554 (2018) 

  • Moser, G.*, Paal, S.G., and Smith, I.F.C. (2017). Measurement system design for leak detection in hydraulic pressurized networks, Structure and Infrastructure Engineering, Taylor and Francis, 13 (7): 918-928, 10.1080/15732479.2016.1225312. Impact Factor: 2.62 (2019)

  • Moser, G.*, Paal, S.G., Jlelaty, D.**, and Smith, I.F.C. (2016). An electrical network for evaluating monitoring strategies intended for hydraulic pressurized networks, Advanced Engineering Informatics, Elsevier, 30 (4), 672-686, 10.1016/j.aei.2016.09.003.

  • Moser, G.*, Paal, S.G., and Smith, I. (2015). Performance comparison of reduced models for leak detection in distribution networks, Advanced Engineering Informatics, Elsevier, 29 (3), 714-726, 10.1016/j.aei.2015.07.003. Impact Factor: 3.879 (2020) 

  • Paal, S.G., Jeon, J., Brilakis, I., and DesRoches, R. (2015). Automated Damage Index Estimation of Reinforced Concrete Columns for Post-Earthquake Evaluations, Journal of Structural Engineering, ASCE, 141 (9), (September 2015), 10.1061/(ASCE)ST.1943-541X.0001200. Impact Factor: 2.528 (2018) 

  • Koch, C., Paal, S.G., Rashidi, A., Zhu, Z., König, M., and Brilakis, I. (2014). Achievements and Challenges in Vision-Based Inspection of Large Concrete Structures, Advances in Structural Engineering, Sage, 17 (3): 303-318, 10.1260/1369-4332.17.3.303. Impact Factor: 1.416 (2019) 

  • German, S., Jeon, J.-S., Zhu, Z., Bearman, C., Brilakis, I., DesRoches, R., and Lowes, L. (2013). Machine Vision Enhanced Post-Earthquake Inspection, Journal of Computing in Civil Engineering, ASCE, 27 (6), 622-634, 10.1061/(ASCE)CP.1943-5487.0000333. Impact Factor: 2.554 (2018) 

  • German, S., Brilakis, I., and DesRoches, R. (2012). Rapid Entropy-Based Detection and Properties Measurement of Concrete Spalling with Machine Vision for Post-Earthquake Safety Assessments, Advanced Engineering Informatics, Elsevier, 26 (4), 846-858, 10.1016/j.aei.2012.06.005. Impact Factor: 3.879 (2020) 

  • Zhu, Z., German, S., and Brilakis, I. (2011). Visual Retrieval of Concrete Crack Properties for Automated Post-Earthquake Structural Safety Evaluation, Journal of Automation in Construction, Elsevier, 20 (7): 874-883, 10.1016/j.autcon.2011.03.004. Impact Factor: 5.669 (2020)

  • Brilakis, I., German, S., and Zhu, Z. (2011). Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure, Journal of Computing in Civil Engineering, ASCE, 25 (5): 388-393, 10.1061/(ASCE)CP.1943-5487.0000104. Impact Factor: 2.554 (2018) 

  • Zhu, Z., German, S., and Brilakis, I. (2010). Detection of Large-Scale Concrete Columns for Automated Bridge Inspection, Journal of Automation in Construction, Elsevier, 19 (8), 1047-1055, 10.1016/j.autcon.2010.07.016. Impact Factor: 5.669 (2020)

Invited presentations

• • Paal, S.G. (2022). An argument in favor of machine learning in earthquake engineering, 12NCEE, Salt Lake City, Utah, June, 30, 2022 (Invited Presentation).

  • Paal, S.G. (2022). Physics-informed prediction of wind pressures on low-rise residential infrastructure, ASCE Structures Congress, Atlanta, Georgia, April 21, 2022.

  • Paal, S.G. (2020). Leveraging existing knowledge and artificial intelligence to enhance our understanding of structural performance, Purdue University, Virtual, November 18, 2020 (Invited Presentation).

  • Paal, S.G. Leveraging existing knowledge and artificial intelligence to enhance our understanding of structural performance, NIST, Virtual, November 18, 2020 (Invited Presentation).

  • Paal, S.G. Leveraging existing knowledge and artificial intelligence to enhance our understanding of structural performance, George Mason University, Virtual, November 18, 2020 (Invited Presentation).

  • Paal, S.G. Evaluating the extent of impact artificial intelligence will have in civil engineering, ERDC, Virtual, October 16, 2020.

  • Paal, S.G. Leveraging existing knowledge and artificial intelligence to enhance our understanding of structural performance, University of Illinois Urbana-Champaign, Virtual, September 24, 2020 (Invited Presentation).

  • Paal, S.G. Integrating traditional civil engineering practices with artificial intelligence: A hybrid approach to enhancing our understanding of structural behavior, American Concrete Institute Strategic Development Council, Virtual, August 25, 2020 (Invited Presentation).

  • Paal, S.G. Augmenting existing civil engineering practices with artificial intelligence: bringing forth the next-generation of “physics-based models”, Software Developer’s Cartel, Bryan, TX, September 17, 2019. 

  • Paal, S.G. Machine learning for seismic performance of reinforced concrete components, DesignSafe-QuakeCoRE Cyberinfrastructure Workshop, Nelson, New Zealand September 3, 2019.

  • Paal, S.G. Integrating traditional civil engineering practices with artificial intelligence: A hybrid approach to enhancing our understanding of structural behavior, American Concrete Institute Strategic Development Council Technology Forum 46, Pittsburgh, Pennsylvania, August 28, 2019.

  • Paal, S.G. Artificial intelligence: it’s just begging us to make the most out of Design Safe, NSF-CMMI Annual Site Visit, Natural Hazards Engineering Research Infrastructure: Cyberinfrastructure, Texas Advanced Center of Computing (TACC), The University of Texas, Austin, TX May 30, 2019.

  • Luo, H.* and Paal, S.G. Performance comparison between formulas and AI-based models in predicting shear strength of RC columns, SEI Structures Congress, Orlando, FL, April 26, 2019.

  • Paal, S.G. The Future of Structural Sensing, SEI Structures Congress, Orlando, FL, April 26, 2019.

  • Paal, S.G. Data-driven disaster science: an artificially intelligent approach to performance-based engineering, National Science Foundation International Workshop to Develop Research Campaigns, Interdisciplinary Teams, and Disruptive Technologies for the NHERI 5-Year Science Plan for Natural Hazards, Alexandria, VA, March 18, 2019.

  • Paal, S.G. A data-driven approach to post-earthquake damage assessments, ACI Innovation in Concrete Construction: Use of New Technology in Post-extreme Event Reconnaissance, American Concrete Institute (ACI), Online Webinar, February, 2019.

  • Paal, S.G. Exploring the usefulness of available experimental data: a machine learning-focused approach to predicting seismic capacity, Department of Civil & Environmental Engineering, Rice University, Houston, Texas, November 2018.

  • Paal, S.G. Machine learning-based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals, Applied Computing and Mechanics Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, September 7, 2018.

  • Paal, S.G., Elwany, A., Kalantar, N., Rybkowski, Z., and Grasley, Z. 3D Printing for Civil Infrastructure Construction, Blue Sky Competition, 2018 North American Manufacturing Research Conference/Manufacturing Science and Engineering Conference, College Station, Texas, June 20, 2018. **Winner of 2nd Annual David Dornfeld Manufacturing Vision Award**

  • Aghababaei*, M., Koliou, M., and Paal, S.G. Damage Assessment of Buildings in the Wake of Hurricane Harvey, EMI Objective Resilience Committee Student Competition, Engineering Mechanics Institute (EMI), MIT, Boston, MA, May 29, 2018.

  • Paal, S.G.  and Miller*, E. Automated Bridge Inspection Procedures via a Hybrid Machine Vision-Machine Learning Technique, Mini-symposium MS74: Vision-based Studies in Structural Health Monitoring, Engineering Mechanics Institute (EMI), MIT, Boston, MA, May 30, 2018.

  • Paal, S.G. Augmented Reality: Augmenting Human Intelligence with Artificial Intelligence in Civil Design and Construction, Infrastructure Advancement Institute, San Antonio, Texas, October 31, 2017.

  • German, S., Brilakis, I., and DesRoches, R. Machine Vision Damage Detection of RC Columns for Rapid Post-Earthquake Safety Assessment. International Conference on Earthquake Engineering Research Challenges in the 21st Century, Harbin, China, May 21, 2012. 

  • Koch, C., German, S., Rashidi, A., Zhu, Z., König, M. and Brilakis, I. Achievements and Challenges in Vision-based Inspection of Large Concrete Structures. 1st International Conference on Performance-based and Life-cycle Structural Engineering, Hongkong, Hongkong, 2012.

Book chapters

• • Luo, H.* and Paal. S.G. (2022). “Probabilistic seismic analysis of reinforced concrete frames using artificial intelligence-enhanced mechanical model.” Chapter of “Seismic Evaluation, Damage, and Mitigation of Structures,” Elsevier.

  • Paal, S.G., Vick, S. and Kopsida, M. (2020). “Use Cases for Architects and Engineers.” Chapter of “Infrastructure Computer Vision,” Edited by: Brilakis, I. and Haas, C., Elsevier.

  • Tang, P., Vick, S., Chen, J. and Paal, S.G. (2020). “Surveying, Geomatics, and 3D Reconstruction.” Chapter of “Infrastructure Computer Vision,” Edited by: Brilakis, I. and Haas, C., Elsevier.

  • Koch, C., Zhu, Z., Paal, S.G. and Brilakis, I. (2015). “Machine Vision Techniques for Condition Assessment of Civil Infrastructure.” Chapter of “Advances in Computer Vision and Pattern Recognition,” 48(2015); pgs. 351-375, Springer, ISSN: 2191-6586, DOI: 10.1007/978-1-4471-6741-9_11541.

  • Koch, C., Zhu, Z., Paal, S.G., and Brilakis, I. (2015). “Machine Vision Techniques for Condition Assessment of Civil Infrastructure.” Chapter of “Integrated Imaging and Vision Techniques for Industrial Inspection: Advances in Computer Vision and Pattern Recognition,” Edited by: Liu, Z., Ukida, H., Ramuhalli, P. and Niel, K., Springer, London, ISBN 978-1-4471-6741-9, 541 pgs, 10.1007/978-1-4471-6741-9_11.

  • German, S., Brilakis, I., and DesRoches, R. (2011). “Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations.” Chapter of “Modern Methods in Advances in Structural Engineering and Construction,” Edited by: Cheung, S.O., Yazdani, S., Ghafoori, N. and Singh, A. Research Publishing Services, ISBN 978-981-08-7920-4, 1350 pgs. 

Conference proceedings

• • Moser, G.*, Paal, S.G., and Smith, I.F.C. (2015). “Using electrical resistance networks to enhance performance assessments of water distribution networks.” Proceedings of the 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, 1-3 July 2015, Torino, Italy.

  • Paal, S.G. and Smith, I.F.C. (2015). “Error Assessment of Machine Vision Techniques for Object Detection and Evaluation.” Proceedings of the ASCE International Workshop on Computing in Civil Engineering, 21-23 June 2015, Austin, Texas, USA.

  • Paal, S.G., Brilakis, I., and DesRoches, R. (2014). “Automated computer vision-based detection of exposed transverse reinforcement for post-earthquake safety assessments.” Proceedings of the 6th World Conference on Structural Control and Monitoring, 15-17 July 2014, Barcelona, Spain.

  • Paal, S.G., Zhu, Z, Brilakis, I., and DesRoches, R. (2014). “Automated Crack Pattern Measurements for Rapid Post-Earthquake Safety Assessment.” Proceedings of the 21st International Workshop: Intelligent Computing in Engineering, 16-18 July 2014, Cardiff, UK.

  • Moser, G.*, Paal, S.G., and Smith, I. (2014). “Performance comparisons of reduced network models for leak region detection in water distribution networks.” Proceedings of the 21st International Workshop: Intelligent Computing in Engineering, 16-18 July 2014, Cardiff, UK.

  • German, S., Brilakis, I., and DesRoches, R. (2012). “Comprehensive property retrieval and measurement of concrete spalling using machine vision for post-earthquake safety assessments.” SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 26 April 2012, San Diego, CA. 

  • German, S., Brilakis, I., and DesRoches, R. (2012). “Machine Vision Damage Detection of RC Columns for Rapid Post-Earthquake Safety Assessment.” Proceedings of the International Conference on Earthquake Engineering Research Challenges in the 21st Century, 18-21 May 2012, Harbin, China.

  • Koch, C., German, S., Rashidi, A., Zhu, Z., König, M., and Brilakis, I. (2012). “Achievements and Challenges in Vision-based Inspection of Large Concrete Structures.” In Proceedings of the 1st International Conference on Performance-based and Life-cycle Structural Engineering, Hongkong, Hongkong. (Invited Paper).

  • Brilakis, I., Zhu, Z., and German, S. (2011). “Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure.” Proceedings of the 2011 NSF Engineering Research and Innovation Conference, 4-7 January 2011, Atlanta, GA.

  • DesRoches, R., Brilakis, I., Lowes, L., Zhu, Z., German, S., and Roberts, S. (2011). “Machine Vision Enhanced Post-Earthquake Inspection and Rapid Loss Estimation.” Proceedings of the 2011 NSF Engineering Research and Innovation Conference, 4-7 January 2011, Atlanta, GA.

  • Lowes, L., DesRoches, R., Brilakis, I., Parra-Montesinos, G., Swanson, D., Weigand, J., Pugh, J., and German, S. (2011). “Collection of Detailed Damage Data to Support Development of Rapid Condition Assessment Methods.” ASCE Structures Congress, 14-16 April 2011, Las Vegas, NV.

  • German, S., Brilakis, I., and DesRoches, R. (2011). “Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations.” Modern Methods and Advances in Structural Engineering and Construction Conference, 21-26 June 2011, Zurich, Switzerland.

  • German, S., Roberts, S., Zhu, Z., DesRoches, R., and Brilakis, I. (2011). “Automated Detection of Significant Damage in Post-Earthquake RC Structures for Collapse Limit State Evaluations.” 2011 COMPDYN Computational Methods in Structural Dynamics and Earthquake Engineering Conference, 25-28 May 2011, Corfu, Greece.

  • Brilakis, I. and German, S. (2010). “Pattern Recognition Models for Smarter Infrastructure Sensing.” Proceedings of the Workshop on Data Mining for Smarter Infrastructure, 2010 SIAM International Conference on Data Mining, 29 April-1 May 2010, Columbus, OH.

  • Zhu, Z., German, S., and Brilakis, I. (2010). “Large Scale Column Detection for Bridge Inspection.” Proceedings of the ASCE Construction Research Congress, 8-11 May 2010, Alberta, CA. 

  • Zhu, Z., German, S., Roberts, S., Brilakis, I., and DesRoches, R. (2011). “Machine Vision Enhanced Post-Earthquake Inspection.” Proceedings of the ASCE International Workshop on Computing in Civil Engineering, 19-22 June 2011, Miami, FL.

  • Zhu, Z., German, S., and Brilakis, I. (2010). “Visual Recognition and Assessment of Concrete Crack Properties,” Proceedings of the 2010 International Conference on Computing in Civil and Building Engineering joint with the 17th EG-ICE Workshop on Intelligent Computing in Engineering, 30 June – 2 July 2010, Nottingham, UK.