Chengbo Ai

Assistant Professor

University of Massachusetts Amherst

United States

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About

Dr. Chengbo Ai is an Assistant Professor at the University of Massachusetts Amherst, USA. His research focuses on advanced applications of LiDAR technology and deep learning methodologies in transportation infrastructure, particularly in railway systems. Recent publications include work on digital twin technologies, semantic segmentation, and automated detection frameworks aimed at enhancing the safety and efficiency of rail operations.

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Collaborators (5)

Alyssa Ryan

Assistant Professor

University of Arizona

UNITED STATES

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Yihao Ren

North Dakota State University

UNITED STATES

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Angelos Filippatos

Assistant Professor

University of Patras

GREECE

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Eleni Christofa

University of Massachusetts Amherst

UNITED STATES

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Francis Tainter

Research Assistant Professor

University of Massachusetts Amherst

UNITED STATES

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Chengbo Ai

Research Interests

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About

Articles (28)

LiDAR-Simulated Multimodal and Self-Supervised Contrastive Digital Twin Approach for Probabilistic Point Cloud Generation of Rail Fasteners

Advanced Deep Learning-Based Hybrid Rail Extraction Algorithm Leveraging LiDAR Technology

Attention-guided triplet framework for synthetic data generation and semantic segmentation of railway fasteners under data scarcity and disparity

Automated flatness quantization and assessment for tunnel initial support based on terrestrial laser scanning

Automation railway fastener tightness detection based on instance segmentation and monocular depth estimation

EXPLORING THE IMPACT OF DIGITAL TWIN TECHNOLOGY IN INFRASTRUCTURE MANAGEMENT: A COMPREHENSIVE REVIEW

Enhancing Disaster Response Planning: A Framework for Bicycle and Pedestrian Transportation

Hybrid Local-Feature-Based Approach for Automated Rail Extraction from Lidar Data

Intelligent Multitasking Framework for Boundary-Preserving Semantic Segmentation, Width Estimation, and Propagation Modeling of Concrete Cracks

Multimodal geometric AutoEncoder (MGAE) for rail fasteners tightness evaluation with point clouds & monocular depth fusion

Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks

An Automated Pavement Marking Retroreflectivity Condition Assessment Method Using Mobile LiDAR and Video Log Images

Hybrid Pixel‐Level Crack Segmentation for Ballastless Track Slab Using Digital Twin Model and Weakly Supervised Style Transfer

Point cloud generation for critical transportation infrastructure through Bézier curve

Automated detection and quantification of pavement cracking around manhole

Digital reconstruction of railway steep slope from UAV+TLS using geometric transformer

Network-level crash risk analysis using large-scale geometry features

Terrestrial laser scanning-assisted roughness assessment for initial support of railway tunnel

Developing a Geospatial Safety Analysis Tool: A Systematic Approach to Identify Safety-Critical Horizontal Curve Segments and Hazardous Contributing Factors

Bicyclist and Motorist Behavior at Bike Boxes

Target-free recognition of cable vibration in complex backgrounds based on computer vision

Target-free recognition of cable vibration in complex backgrounds based on computer vision

A framework for mode classification in multimodal environments using radar-based sensors

Driver performance at horizontal curves: bridging critical research gaps to increase safety

Rail gage-based risk detection Using iPhone 12 pro

Network-Level Guardrail Extraction Based on 3D Local Features from Mobile LiDAR Sensor

An Automated Sound Barrier Inventory Method Using Mobile LiDAR

Using 3D laser scanning for estimating the capacity of corroded steel bridge girders: Experiments, computations and analytical solutions

Collaborators (5)

Alyssa Ryan

Yihao Ren

Angelos Filippatos

Eleni Christofa

Francis Tainter

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