Faculty Directory

• Machine learning modeling of geotechnical data
• 3D geological model with AI-predicted point cloud data
• Portable self-centering modular panels for rapid structural strengthening
• Mass timber structures
• Stainless steel bridge bearings
• Self-centering wood structures
• Train-induced ground vibration and impact on buildings
• Drone based bridge compoment inspections

Research facility:

Structures Lab has equipments to conduct large scale structures testing such as fatigue testing of bridge structures and mass timber structures, low cycle fatigue behavior test of stainless steel structures components, self-centering modular panels and self-centering steel eccentrically braced frames. The mission of the Structural Lab is to fulfill the emerging need in the development and validation of resilient modular structures and sensor/structural health monitoring technology for civil infrastructures.

Structures Lab testing facility includes large-scale structure testing systems including a 1000 sq. ft. strong floor, 3-DOF servo-hydraulic controlled test system and 400-kips SATEC universal testing machine. There are also loading frames for large scale structural testing. The large scale structural testing facility has a 3-DOF servo-hydraulic controlled test system including three MTS servo-controlled actuators (two 55 kips load capacity, one 110 kips load capacity), a 60-GPM hydraulic power supply, a 4-channel servo-control system with FlexTest GT test software from MTS, three 50-GPM service manifolds. A 108-channel data acquisition system from Pacific Instruments (model 6005, shown in Figure 1-(b)) is available for instrumentation data collection (32 strain gage channels, 44 thermocouple channels, 16 accelerometer channels, and 16 LVDT channels) in lab and field test. Available transducers in the lab include load cells, LVDT, ultrasonic displacement transducer, tilt sensor, strain gages, thermocouples and accelerometers. Reaction frames are set up for different load configuration (e.g., load frame in Figure 1-(a) is set up for applying vertical load on test specimen such as concrete beam). This facility has been used in the past two years for fatigue test of steel structures, prestressed concrete beams, and seismic performance characterization test of steel braces and dampers. An overhead crane with 5-tonf lift capacity is used to move test specimens around the lab. A SATEC servo-hydraulic universal testing machine is available for applying both tension and compression load to specimens (e.g., concrete cylinders) up to 400 kips.

Dr. Zhang research program has received external funding support totaling $6.8 million from diverse sources including the National Science Foundation (11 competitively awarded NSF grants in total), U.S. Department of Transportation and Federal Highway Administration, MD State Highway Adminstration and other sponsoring agencies. 

Most recent grants are: 

1. "Implementing Machine Learning with Highway Datasets", PI,  Implemented recent advance in machine learning methods including neural nets, reinforcement learning and computer vision methods in design and construction, materials test data, management, operating optimization and field inspection. Detecting highway surface feature polygon using machine learning (instance segmentation) model on 1-m resolution western four Maryland counties' Lidar DEM data. Developed and tested new machine learning models for datasets of interest including drilling and pavement data, NDE of bridge deck, project duration and highway right-of-way image datasets. 

2. "Use of Stainless-Steel Bridge Bearings with Steel Girder Bridges",  PI, addressing the question on whether it is viable option to replace the standard metal bearings with stainless steel and eliminate any possibility of rusting. The outcomes of this project include research survey results and literature search findings and the future adoption of stainless steel bearings or replacing conventional steel plates (masonry and sole plates, anchor rods and washers) in elastomeric bridge bearings has a strong potential of resulting in the elimination of maintenance efforts resulting from bearing replacements, which might substantially reduce the life-cycle cost of bridge bearings. This study also compared the life cycle cost of a stainless steel bridge bearing (maintenance free) and that of elastomeric bridge bearing with maintenance over its life span.

• ENCE353 Introduction to Structural Analysis
• ENCE444 Experimental Methods in Geotechnical and Structural Engineering
• ENCE613 Structural Dynamics
• ENCE688A Sensing and Systems Control
• ENCE688W Design of Timber Structures
• ENCE710 Steel Structures Design
• ENCE715 Earthquake Engineering

Selected recent publications (see my google scholar page for more publications): 

• Rezvan, P, Zhang, Y. (2024). Near-fault ground motion effect on self-centering modular bracing panels considering soil-structure interaction,” Advances in Structural Engineering. Volume 27, Issue 3, https://doi.org/10.1177/136943322312220.

• Rezvan, P, Zhang, Y. (2023). Digital Twin Model for Regional-Scale Seismic Resilience Assessment of School Buildings with Modular Retrofit Panel System. Journal of Tongji University, 51(12), 1879- 1899.   

• Rezvan, P., Zhang, Y. (2023). Seismic design and performance study of self-centering moment resisting frames with sliding rocking beams and preloaded disc springs. Earthquake Engineering and Structural Dynamics.  10.1002/eqe.3859

• Rezvan, P. and Zhang, Y. (2022). “Nonlinear seismic performance study of D-type self-centering eccentric braced frames with sliding rocking link beams,” Earthquake Engineering and Structural Dynamics, 51(4): 875 - 895.

• Chu, G., Wang, W. and Zhang, Y. (2022). “Experimental and Numerical Study of Near-Fault Seismic Performance of 2-Story Steel Framed Building with Self-Centering Modular Panels,” ASCE J. Structural Engrg., 148(6).

• Liu, H. and Zhang, Y. (2020). "Deep Learning based Crack Damage Detection Technique for Thin Plate Structures using Guided Lamb Wave Signals," Smart Materials and Structures, 29(1): 015032. (Top Cited Paper Award - North American, by IOP Publishing 2023)

• Liu, H. and Zhang, Y. (2020). "Bridge Condition Rating Data Modeling using Deep Learning Algorithm," Structure and Infrastructure Engineering, 16(10): 1447-1460, https://doi.org/10.1080/15732479.2020.1712610

• Liu, H. and Zhang, Y. (2019). "Deep learning-based brace damage detection for concentrically braced frame structures under seismic loadings.” Advances in Structural Engineering, https://doi.org/10.1177/1369433219859389.
• Keivan, A. and Zhang, Y. (2019). “Seismic performance evaluation of self-centering K-type and D-type steel eccentrically braced frame systems,” Engineering Structures, 184: 301-317. 
• Liu, H. and Zhang, Y. (2019). “Image-driven structural steel damage condition assessment method using deep learning algorithm.” Measurement,133: 168-181.
• Keivan, A. and Zhang, Y. (2019). “Nonlinear seismic performance of Y-type self-centering steel eccentrically braced frame buildings,” Engineering Structures, 179(15): 448-459. 
• Tong, L, Zhang, Y, Zhou X, Keivan A, Li R. (2019). “Experimental and analytical investigation of D-type self-centering steel eccentrically braced frames with replaceable hysteretic damper,” ASCE J Structural Engrg, 145(1): 04018229.
• Zhang, Y. and Liu, H. (2019). “Experimental study of vibration mitigation of mast arm signal structures with particle-thrust damping based tuned mass damper.” Earthquake Engineering and Engineering Vibration, 18(1): 219-231.
• Zhang, Y., Ayyub, B., & Huang, H. (2018). "Enhancing Civil Infrastructure Resilience with Structural Health Monitoring." Ch.1 in Resilience Engineering for Urban Tunnels, ASCE. 
• Tong, L., Zhang, Y., Zhang, L., Liu, H., Zhang, Z., and Li, R. (2018). “Ductility and energy dissipation behavior of G20Mn5QT cast steel shear link beams under cyclic loading.” Journal of Constructional Steel Research, 149, 64-77.
• Wang, Z., Zhang, Y., Wang, Y., Du, X. and Yuan, H. (2018). “Numerical study on fatigue behavior of tubular joints for signal support structures,” J. Constructional Steel Research, 143: 1-10.
• Wang, W., Kong J., Zhang, Y., Chu G. and Chen, Y. (2017). “Seismic behavior of self-centering modular panel with slit steel plate shear walls: experimental testing,” ASCE J. Structural Engrg., 144(1).
• Wang, W., Du, X., Zhang, Y. and Chen, Y. (2017). “Experimental Investigation of beam-through steel frames with self-centering modular panels,” ASCE J. Structural Engrg., 143(5).
• Xu, X., Zhang, Y. and Luo, Y. (2016). “Self-centering eccentrically braced frames using shape memory alloy bolts and post-tensioned tendons,” J. Constructional Steel Research, 125: 190–204.
• Xu, X., Zhang, Y. and Luo, Y. (2016). “Self-centering modularized link beams with post-tensioned shape memory alloy rods,” Engineering Structures, 112: 47–59.
• Zhang, Y. and Bai, L. (2015). “Rapid structural condition assessment using radio frequency identification (RFID) based wireless strain sensor,” Automation in Construction, 54: 1-11.
• Zhang, Y., Shi, F., Song, J., Zhang, X., Yu, S. (2015). “Hearing characteristics of Cephalopods: modeling and environmental impact study,” Integrative Zoology, 10: 141-151.
• Li, R., Zhang, Y. and Tong, L.W. (2014). “Numerical study of the cyclic load behavior of AISI 316l stainless steel shear links for seismic fuse device,” Frontiers of Structural and Civil Engineering, 8(4): 414-426.
• Li, Z. and Zhang, Y. (2014). “Fatigue life prognosis study of welded tubular joints in signal support structures,” International Journal of Steel Structures, 14(2): 281-292.
•  Zhou, C. and Zhang, Y. (2014). “Acoustic emission source localization using coupled piezoelectric film strain sensors” J. of Intelligent Material System and Structures, 25(16): 2082-2092.
• Zhou, C. and Zhang, Y. (2014). “Near-field acoustic emission sensing performance of piezoelectric film strain sensor,” Research in Nondestructive Evaluation, 25(1): 1-19. (DOI:10.1080/09349847.2013.810318)
•  Li, Z. and Zhang, Y. (2014). “Extreme value theory based structural health prognosis method using reduced sensor data,” Structures and Infrastructure Engineering, 10(8): 988-997. DOI: 10.1080/15732479.2013.774427.
• Hu, X., and Zhang, Y. (2013). “Ductility demand of partially self-centering structures under seismic loading: SDOF systems,” Journal of Earthquake and Structures, 4(4), 365-381.
• Moghaddasi B, N.S. and Zhang, Y. (2013). “Seismic analysis of diagrid structural frames with shear-link fuse devices,” Earthquake Engineering and Engineering Vibration, 12(3), 463-472.
• Bai, L. and Zhang, Y. (2013). “Nonlinear dynamic behavior of steel framed roof structure with self-centering members under transient wind loading,” Engineering Structures, 49(4): 819–830.
• Hu, X., Zhang, Y. and Moghaddasi, N. (2012). “Seismic performance of reinforced concrete frames retrofitted with self-centering hybrid wall,” Advances in Structural Engineering, 15(12): 2139-2151.
• Bai, L. and Zhang, Y. (2012). “Collapse fragility assessment of steel roof framings with force limiting devices under transient wind loading,” Frontiers of Structural and Civil Engineering, 6(3): 199-209. DOI: 10.1007/s11709-012-0168-4.
• Moghaddasi B, N.S., Zhang, Y. and Hu, X. (2012). “Seismic retrofitting of reinforced concrete frame structures using GFRP-tube-confined-concrete composite braces,” Earthquake Engineering and Engineering Vibration, 11(1): 91-105.
• Mercado, M. and Zhang, Y. (2012). “A hybrid simulation testbed for realistic evaluation and characterization of NDE and sensor technology,” Journal of Bridge Engineering, 17(6): 907-920.
• Zhou, C. and Zhang, Y. (2012). “Particle filter based noise removal method for acoustic emission signals,” Mechanical Systems and Signal Processing, 28(1-2): 63–77.
• Zhang, Y. and Hu, X. (2010). “Self-centering seismic retrofit scheme for reinforced concrete frame structures: SDOF system study,” Earthquake Engineering and Engineering Vibration, 9(2): 271-283.   DOI 10.1007/s11803-010-0012-6.
• Zhang, Y., Hu, X. and Zhu, S. (2009). “Seismic performance of benchmark base isolated bridges with superelastic Cu-Al-Be wire damper,” Structural Control and Health Monitoring, 16: 668-685.  

Names in bold font indicate the correspondence author of the paper.