5 citations · 14 across the 32 of their papers we have counts for
34 papers
Repair Lot Skyline: A Weighted Constraint Satisfaction Approach to Pavement Repair Optimization from Geospatial Hazard Density
Takato Yasuno, Keita Kobayashi, Ryuta Sakaguchi +1
Pavement agencies must translate a spatially distributed distress inventory into a bounded, actionable repair-lot plan: accident-critical defects (potholes) must always be addresse…
Convergence-Aware Pareto Selection of Covariate Scaling Transformations for Markov Deterioration Hazard Models: Evidence from Bridge Inspection Data
Takato Yasuno, Keita Kobayashi, Ryuta Sakaguchi
Maximum likelihood estimation of Markov deterioration hazard models for infrastructure asset management is sensitive to the numerical conditioning of explanatory covariates, yet pr…
Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA
Takato Yasuno
Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye. Expert diagnosis rel…
Multimodal CoLRAG-TF: Triple-Filtered Retrieval for Complex PDFs
Takato Yasuno
Retrieval-augmented generation (RAG) over heterogeneous PDF collections remains challenging due to multimodal content, domain-specific terminology, and the need for multi-hop reaso…
Fine-Tuning Vision-Language Models for Understanding Current Damage and Scoring Priority with Quality Guard Agent
Takato Yasuno
Bridge inspection in Japan requires mandatory visual assessments every five years, yet qualitative damage ratings (levels a-e) assigned by different engineers exhibit significant i…
Understanding Deterioration Random Effects for Causal Discovery in Infrastructure Management
Takato Yasuno
Infrastructure deterioration poses significant challenges for asset management, yet existing approaches rely on population-averaged models that overlook equipment-specific heteroge…
Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters
Takato Yasuno
Bayesian finite mixture models can identify discrete risk clusters (low-risk vs. high-risk equipment), but face three critical bottlenecks: (1) insufficient degradation signals fro…
Bridge-Centered Metapath Classification Using R-GCN-VGAE for Disaster-Resilient Maintenance Decisions
Takato Yasuno
Daily infrastructure management in preparation for disasters is critical for urban resilience. When bridges remain resilient against disaster-induced external forces, access to hos…
Heterogeneous Graph Importance Scoring and Clustering with Automated LLM-based Interpretation
Takato Yasuno
Urban bridge networks are critical infrastructure whose disruption can cascade into severe impacts on transportation, emergency services, and economic activity. This paper presents…
Quantized Vision-Language Models for Damage Assessment: A Comparative Study of LLaVA-1.5-7B Quantization Levels
Takato Yasuno
Bridge infrastructure inspection is a critical but labor-intensive task requiring expert assessment of structural damage such as rebar exposure, cracking, and corrosion. This paper…
Adapting Methods for Domain-Specific Japanese Small LMs: Scale, Architecture, and Quantization
Takato Yasuno
This paper presents a systematic methodology for building domain-specific Japanese small language models using QLoRA fine-tuning. We address three core questions: optimal training…
Suppressing Domain-Specific Hallucination in Construction LLMs: A Knowledge Graph Foundation for GraphRAG and QLoRA on River and Sediment Control Technical Standards
Takato Yasuno
This paper addresses the challenge of answering technical questions derived from Japan's River and Sediment Control Technical Standards -- a multi-volume regulatory document coveri…
FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
Takato Yasuno
Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance co…
RAPTOR-AI for Disaster OODA Loop: Hierarchical Multimodal RAG with Experience-Driven Agentic Decision-Making
Takato Yasuno
Humanitarian Assistance and Disaster Relief (HADR) operations demand rapid synthesis of multimodal information for time-critical decision-making under extreme uncertainty. Traditio…
Triplet Feature Fusion for Equipment Anomaly Prediction : An Open-Source Methodology Using Small Foundation Models
Takato Yasuno
Predicting equipment anomalies before they escalate into failures is a critical challenge in industrial facility management. Existing approaches rely either on hand-crafted thresho…
Disaster Question Answering with LoRA Efficiency and Accurate End Position
Takato Yasuno
Natural disasters such as earthquakes, torrential rainfall, floods, and volcanic eruptions occur with extremely low frequency and affect limited geographic areas. When individuals…
Multi-stage Bridge Inspection System: Integrating Foundation Models with Location Anonymization
Takato Yasuno
In Japan, civil infrastructure condition monitoring is mandated through visual inspection every five years. Field-captured damage images frequently contain concrete cracks and reba…
Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment
Takato Yasuno
Condition-Based Maintenance (CBM) signifies a paradigm shift from reactive to proactive equipment management strategies in modern industrial systems. Conventional time-based mainte…
Cell Culture Assistive Application for Precipitation Image Diagnosis
Takato Yasuno
In regenerative medicine research, we experimentally design the composition of chemical medium. We add different components to 384-well plates and culture the biological cells. We…
Few-shot Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance
Takato Yasuno
Over the past decade, previous balanced datasets have been used to advance deep learning algorithms for industrial applications. In urban infrastructures and living environments, d…
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses
Takato Yasuno, Masahiro Okano, Junichiro Fujii
Extreme natural disasters can have devastating effects on both urban and rural areas. In any disaster event, an initial response is the key to rescue within 72 hours and prompt rec…
Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs
Takato Yasuno, Masahiro Okano, Junichiro Fujii
Maintaining high standards for user safety during daily railway operations is crucial for railway managers. To aid in this endeavor, top- or side-view cameras and GPS positioning s…
One-class Damage Detector Using Deeper Fully-Convolutional Data Descriptions for Civil Application
Takato Yasuno, Masahiro Okano, Junichiro Fujii
Infrastructure managers must maintain high standards to ensure user satisfaction during the lifecycle of infrastructures. Surveillance cameras and visual inspections have enabled p…
MN-Pair Contrastive Damage Representation and Clustering for Prognostic Explanation
Takato Yasuno, Masahiro Okano, Junichiro Fujii
For infrastructure inspections, damage representation does not constantly match the predefined classes of damage grade, resulting in detailed clusters of unseen damages or more com…