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Li Ai

Assistant Professor

The University of Texas Rio Grande Valley

United States

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About

Dr. Li Ai is a Research Assistant Professor at the University of South Carolina, USA. His research interests encompass advanced sensing techniques, health monitoring of infrastructure, and the application of artificial intelligence in materials assessment. Recent publications include works on acoustic emission sensing, virtual-to-real mapping methodologies, and innovative approaches for evaluating composite materials and infrastructure resilience.

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Collaborators (1)

Hongpeng Fu

UNITED STATES

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Li Ai

Research Interests

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About

Articles (35)

Virtual-to-real mapping: an AE missing signal generation and assisted localization method for defects in CFRP plates

AI-driven detection of failure modes in thermoplastic composites using acoustic emission techniques

Hybrid deep learning model for accurate settlement forecasting of metro tracks under canal diversion engineering

A novel Optuna-VMD-ML framework for enhanced settlement prediction of buildings around foundation pits

Evaluating the impact of aggregate size and reinforcement on alkali-silica reaction in concrete through nondestructive testing techniques

Explainable machine learning and life cycle assessment for sustainable design of fiber-reinforced asphalt concrete

Hybrid learning architecture for high-speed railroad scene parsing and potential safety hazard evaluation of UAV images

Acoustic emission sensing for assessing compressive strain in impacted thermoplastic composites

An anchor-free selective state space framework for real-time tunnel structure health monitoring

Focus issue on AI-driven measurement methods for resilient infrastructure and communities

Lithium slag-ground granulated blast furnace slag based geopolymer: Efflorescence mechanism analysis

Evaluation of impact energy in composites using acoustic emission sensing technique

An automated load determination approach for concrete girder bridges through acoustic emission

Metal surface crack depth laser acoustic emission detection method based on multivariate feature adaptive extraction and cross-modal interaction fusion

Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

Immersed force analysis of fish surface with carangiform locomotion

Acoustic emission monitoring and automated characterization of low-velocity impacts on composite components

Condition assessment of prestressed concrete channel bridge girders using acoustic emission and data-driven methods

Full-scale experimental investigation of prestressed concrete bridge girders strengthened with aluminium channels

Effects of modified multi-walled carbon nanotubes and PVA fibers on mechanical properties and microstructure of lithium slag base geopolymer

MFC-PINN: A method to improve the accuracy and robustness of acoustic emission source planar localization

Assisting Load Rating Testing of Precast Reinforced Concrete Bridge Slab through Digital Twins and Field Monitoring Data

Numerical investigation of three-dimensional incompressible fluid flow in curved elastic tube

Structural health monitoring of stainless-steel nuclear fuel storage canister using acoustic emission

Guest Editorial: AI-driven Monitoring and Condition Assessment for Enhancing Infrastructure Resilience

Exploiting machine learning for controlled synthesis of carbon dots-based corrosion inhibitors

A transfer learning approach for acoustic emission zonal localization on steel plate-like structure using numerical simulation and unsupervised domain adaptation

Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study

Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning

Localizing Damage on Stainless Steel Structures Using Acoustic Emission Signals and Weighted Ensemble Regression-based Convolutional Neural Network

Determination of vehicle loads on bridges by acoustic emission and an improved ensemble artificial neural network

Developing a heterogeneous ensemble learning framework to evaluate Alkali-silica reaction damage in concrete using acoustic emission signals

Evaluation of ASR in concrete using acoustic emission and deep learning

Detection of impact on aircraft composite structure using machine learning techniques

Implementation of Information Entropy, b-Value, and Regression Analyses for Temporal Evaluation of Acoustic Emission Data Recorded during ASR Cracking

Collaborators (1)

Hongpeng Fu

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