About

The Department of Architectural Engineering at Penn State is recognized as a leading institution in the field, with a mission to advance the built environment through the development of world-class architectural engineers and research. The department emphasizes the scientific and engineering aspects of planning, designing, and constructing buildings, providing students with outstanding education and research opportunities. The vision of the department is to lead the world in innovative education and research to achieve high-performing built environments. While specific details about Professor Yuqing Hu's individual research focus, background, or key contributions are not provided in the page text, it can be inferred that the department's overarching goals and research areas align with advancing architectural engineering through innovative and high-quality research and education.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Engineering
• Data Mining
• Environmental science
• Internal medicine
• Medicine
• Reliability engineering
• Control engineering
• Civil engineering
• Environmental health

Selected publications

• UAV detection based on high-order cumulant two-dimensional slice spectrum analysis

  DOAJ (DOAJ: Directory of Open Access Journals) · 2026-04-01

  articleOpen access1st authorCorresponding

  With the widespread deployment of unmanned aerial vehicles in urban environments, security threats caused by illegal intrusions have become increasingly prominent. Detecting such “low-altitude, slow, and small” targets in complex urban scenarios remains a challenging problem. Existing detection methods based on high-order cumulants are capable of suppressing Gaussian noise and characterizing non-Gaussian signal features; however, their detection performance degrades significantly when the transmitter-receiver separation is large or the target echo is weak. To address this limitation, a detection method based on two-dimensional slice spectrum analysis of high-order cumulants was proposed. Specifically, the high-order cumulant expansion function of the received signal was computed to preserve local structural features under different delay combinations. Two-dimensional slices were extracted for spectral analysis, and a frequency-domain energy-based threshold detector was constructed to enhance the separability between the target and background interference. Simulation results demonstrate that, in complex multipath and low signal-to-noise ratio scenarios, the proposed method can maintain a detection probability exceeding 60% within a 500 m<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mo>×</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/E2251E89-C388-4f11-86F6-71A591D09BEB-M002.jpg"><?fx-imagestate width="1.43933344" height="2.28600001"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/E2251E89-C388-4f11-86F6-71A591D09BEB-M002c.jpg"><?fx-imagestate width="1.43933344" height="2.28600001"?></graphic></alternatives></inline-formula>500 m surveillance area, thereby validating its effectiveness and robustness for detecting weak UAV intrusion signals.

  Publisher

• Bypassing Hydrogenation Pathway for Sustainable Nitrate Water Remediation via Direct N─N Coupling

  Angewandte Chemie · 2026-04-20

  article

  ABSTRACT Catalytic nitrate (NO 3 − ) reduction (CNR) to dinitrogen (N 2 ) offers an efficient strategy for remediating nitrogen pollution but is constrained by preferred ammonia (NH 3 ) formation. This selectivity challenge arises because hydrogen atom (H*)‐mediated pathway inherently favors N─H coupling over the desired N─N coupling. Here, we report a formic acid (HCOOH)‐driven proton‐coupled electron transfer (PCET) pathway on a precisely engineered Sn 3 /Pd catalyst. The catalyst design features a synergistic bimetallic interface where Pd sites facilitate HCOOH activation while triangular Sn 3 ensembles selectively adsorb NO 3 − . This direct PCET from HCOOH to NO 3 − achieved a remarkable 96.5% NO 3 − removal and 97.4% N 2 selectivity at environmentally relevant concentrations (100 mg‐N/L). Operando mass spectrometry and density functional theory (DFT) calculations reveal that Sn 3 ensembles thermodynamically favored N─N coupling while also acting as a steric barrier that kinetically impedes H* migration to adsorbed N* intermediates, effectively suppressing NH 3 formation. Furthermore, by integrating the CNR process with electro‐synthesized HCOOH, we demonstrated a synergistic technology that slashed the carbon footprint of wastewater treatment by 43.3%, decreasing from 33.50 kg CO 2 ‐eq t −1 to 19.01 kg CO 2 ‐eq t −1 . Our work establishes atomic ensemble engineering as a powerful strategy to steer catalytic pathway through PCET, offering a viable solution for sustainable NO 3 − removal.

  PublisherDOI

• Nonlinear Hall quantum oscillations to probe topological Brown-Zak fermions in graphene moiré systems

  arXiv (Cornell University) · 2026-05-07

  preprintOpen access

  Due to the deep connection with the quantum geometry of electronic Bloch wavefunctions, the second-order nonlinear Hall effect (NLHE) has been an attractive topic since its proposal. However, studies on NLHE under a magnetic field have been lacking. Given that quantum oscillations in the linear response regime have been proven to be useful tools in investigating electronic systems, searching for quantum oscillations in NLHE is of great interest and is expected to provide new avenues to unveil rich quantum geometric properties of novel quasiparticles. Here, we propose a new type of NLHE quantum oscillations and experimentally probe it in graphene moiré systems. It stems from the alternation of the dominant NLHE mechanisms with recurring Bloch states under magnetic field, which enables sensitive detection of Brown-Zak fermions, giving an onset field as low as 0.5 T. Most importantly, when the commensurability condition is satisfied, the nonlinear transport of Brown-Zak fermions is mainly governed by quantum geometric contributions. Our findings not only establish a new type of quantum oscillations, but also demonstrate the first experimental detection of the topological nature of Brown-Zak fermions, shedding light on the exploration of novel topological quasiparticles.

  PublisherDOI

• Fragility analysis of canyon-crossing bridges considering the near-source canyon topographic effect

  Journal of Central South University · 2026-03-16

  article
  PublisherDOI

• Photocatalytic Phenylmethylamine Coupling Reaction of Organic–Inorganic Composites Based on Benzothiophene Polymers and TiO2

  Nanomaterials · 2026-03-19

  articleOpen access

  Benzothiophene polymers, as a class of novel organic semiconductor materials, exhibit significant potential in the field of photocatalysis due to their broad light-responsive range and tunable energy level structures. In this study, a benzothiophene-based polymer organic semiconductor (denoted as P42) was integrated with titanium dioxide (TiO2) via a simple sol–gel method, yielding an organic–inorganic hybrid material. This composite facilitates the modulation of energy level potentials and promotes the effective separation of photogenerated charges, thereby demonstrating remarkable synergistic catalytic performance in the photocatalytic oxidative coupling of benzylamines. By optimizing the ratio of organic to inorganic components and various photocatalytic reaction conditions, the hybrid material 1.7%P42-TiO2, containing 1.7 wt% of the dithiophene polymer without any metal cocatalysts, exhibited outstanding performance under an air atmosphere and visible light irradiation after 12 h. It achieved a yield of over 88.7% and a selectivity exceeding 89.8% in the synthesis of N-benzoylaniline, significantly surpassing the performance of pure TiO2 (52.9% yield, 54.9% selectivity) and P42 (54.4% yield, 54.9% selectivity). Structural and photophysical characterizations, including UV–Vis DRS, XRD, SEM, TEM, and EPR, reveal that the enhanced photocatalytic activity originates from broad visible-light absorption, improved charge separation, and well-matched energy levels. Mechanistic investigations suggest a synergistic pathway involving photoinduced hole oxidation and radical-mediated coupling. This work provides valuable insights and a reference for the solar-driven photocatalytic synthesis of nitrogen-containing platform molecules under mild conditions.

  PublisherOA PDFDOI

• Identification and Geolocation of Pavement Marking Issues Based on Artificial Intelligence and Mobile Phone

  2025-12-11

  articleSenior authorCorresponding

  Artificial intelligence (AI) has been applied to detect faded pavement markings. However, previous studies only focus on particular types of pavement markings and do not involve the geolocations of identified marking issues in detection. Therefore, this paper proposes a lightweight approach leveraging deep learning and a mobile phone to identify and locate faded markings. First, videos are collected by a mobile phone mounted on the front windshield of a vehicle and then converted into images. A total of ~7,000 images with high quality capturing the faded markings are selected manually, and the faded markings are classified into two classes based on color and labeled as white faded and yellow faded markings. Then, a detection model is developed based on YOLOv8 and performs well in identifying the faded markings in white and yellow, including lane markings, arrow markings, delineators, and crosswalks, with an average precision of 0.90 and recall of 0.87. Additionally, geolocation is collected by an open-source GPX tracker along with the videos. Considering the heterogeneous sampling rates of video (30 frames per second) and GPX tracker (~2 times per second), this study develops another model to estimate and visualize the locations of the identified markings by time-based interpolation, which has a distance error of 0.27 m. The models proposed in this study offer an automatic and affordable solution to inspect pavement markings and locate identified marking issues, enabling efficient maintenance planning of pavement markings and ultimately improving safety for road users.

  PublisherDOI

• A review of future weather data for assessing climate change impacts on buildings and energy systems

  Renewable and Sustainable Energy Reviews · 2025-01-19 · 20 citations

  review
  PublisherDOI

• A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms

  ArXiv.org · 2025-06-02

  preprintOpen access

  Vertical Federated Learning (VFL)-based Traffic State Estimation (TSE) offers a promising approach for integrating vertically distributed traffic data from municipal authorities (MA) and mobility providers (MP) while safeguarding privacy. However, given the variations in MPs' data collection capabilities and the potential for MPs to underperform in data provision, we propose a reliable VFL-based TSE framework that ensures model reliability during training and operation. The proposed framework comprises two components: data provider selection and incentive mechanism design. Data provider selection is conducted in three stages to identify the most qualified MPs for VFL model training with the MA. First, the MA partitions the transportation network into road segments. Then, a mutual information (MI) model is trained for each segment to capture the relationship between data and labels. Finally, using a sampling strategy and the MI model, the MA assesses each MP's competence in data provision and selects the most qualified MP for each segment. For the incentive mechanism design, given the MA can leverage the MI mode to inspect the data quality of MP, we formulate the interaction between MA and MP as a supervision game model. Upon this, we devise a penalty-based incentive mechanism to inhibit the lazy probability of MP, thereby guaranteeing the utility of MA. Numerical simulation on real-world datasets showcased that our proposed framework augments the traffic flow and density prediction accuracy by 11.23\% and 23.15\% and elevates the utility of MA by 130$\sim$400\$ compared to the benchmark.

  PublisherOA PDFDOI

• Reliable Traffic State Estimation via Vertical Federated Learning

  2025-06-08

  article

  Traffic state estimation (TSE) is critical in underpinning the route planning of intelligent transportation systems (ITS). In light of vertical split traffic data might be from various entities, such as municipal authority (MA) and multiple mobility providers (MPs), vertical federated learning (VFL)-based TSE is proposed to resolve the vertical data privacy issue. However, due to discrepancies in data collection and missing data imputation technologies of MPs, the data quality of MPs regarding the same road segment might vary. To this end, we propose a reliable VFL-based TSE framework, including data provider selection and VFL model training. Concretely, given the high-dimension nature of traffic data, the MA will train a tiny mutual information (MI) model for data provider selection. After that, the MA will split the well-trained MI model into sub-models and top models and deploy them on MPs and MA, respectively, so as to preserve the nature of VFL. Eventually, upon MI models, the most representative MP of each road segment is selected for a reliable VFL model. Numerical simulation on real-world datasets shows that our framework augments the performance of traffic flow and traffic density by 11.23% and 21.15% in comparison with the baseline without data provider selection.

  PublisherDOI

• Flexural Performance of Cracked UHPC under the Coupled Actions of Sustained Loading and Corrosive Environments

  Journal of Materials in Civil Engineering · 2025-09-30 · 1 citations

  article

  This study investigated the flexural behavior of ultrahigh-performance concrete (UHPC) subjected to sustained flexural loading and environmental exposure. Four-point bending tests were conducted on precracked UHPC specimens cured for 30 days under continuous loads of 0%, 40% and 60% of the ultimate flexural strength, and exposed to three environments: air, NaCl solution, and water. Key flexural properties, including flexural strength, deflection, and toughness, were measured and analyzed to determine the effects of load–environment coupling on UHPC performance. The results showed that sustained loading negatively impacted UHPC’s flexural properties, and higher loads resulted in greater reductions of strength and toughness. Water exposure facilitated the most significant strength recovery, due to secondary hydration and the formation of C─ S─ H gel and CaCO3. In contrast, exposure to chloride environments caused steel fiber corrosion, which diminished the overall recovery of the material.

  PublisherDOI

Recent grants

• FW-HTF-R/Collaborative Research: FAIR4WISE: Future AI and Robotics for Women in Smart Engineering

  NSF · $560k · 2022–2026

Frequent coauthors

• Jiannan Cai

  The University of Texas at San Antonio

  27 shared

• Jianli Chen

  25 shared

• Chen Xia

  Pennsylvania State University

  20 shared

• Usman Rasheed

  The University of Texas at San Antonio

  18 shared

• Shuai Li

  Research Institute of Petroleum Exploration and Development

  18 shared

• Xiaoyun Liang

  The University of Texas at San Antonio

  13 shared

• Robert Leicht

  Pennsylvania State University

  13 shared

• Jiang Jin

  9 shared

Labs

• Architectural Engineering LabPI

Awards & honors

• Outstanding Engineering Alumni Award
• ASAE Early Career Impact Award
• Penn State Engineering Alumni Society Awards
• Penn State Alumni Association Awards