arXiv Machine Learning

Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis

arXiv:2608. 00956v1 Announce Type: new Abstract: This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design.

arXiv Machine Learning

2d ago

Probabilistic indirect models for undrained shear strength: addressing significant data missing and variability with advanced imputation and machine learning techniques

arXiv:2608. 13934v1 Announce Type: new Abstract: Accurate prediction of undrained shear strength (su) is crucial for geotechnical design, but is often hampered by substantial uncertainty in traditional empirical methods.

By Haibin Xiong, Shaoheng Dai, Peng Lan, Xuzhen He, Chenxi Tong, Sheng Zhang, Daichao Sheng

arXiv Machine Learning

Aug 11

Distribution-Free Conformal Prediction for Steel Fatigue Strength: Marginal Validity Is Not Enough

arXiv:2608. 07589v1 Announce Type: cross Abstract: Predicting fatigue failure in steel components experimentally is costly because it requires testing across multiple compositions and processing conditions.

By Irene Boruah

safety

arXiv Machine Learning

Jun 8

Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag

arXiv:2606. 06765v1 Announce Type: cross Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challenge in cementitious materials, particularly for alkali-activated materials with variable precursor and activator chemistry.

By Qiyao He, Zhanzhao Li, Kai Gong

robotics

arXiv Machine Learning

Jul 21

Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis

arXiv:2607. 17024v1 Announce Type: new Abstract: Air pollution and climate-related stressors are increasingly important concerns for respiratory health, especially in settings with unequal environmental exposure and healthcare capacity.

By Maede Azani Hassan Abadi, Shouyi Wang

arXiv Machine Learning

Jun 16

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data

arXiv:2605. 27618v2 Announce Type: replace Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable.

By Tom\'as Pereira, Jo\~ao Vitorino, Eva Maia, Isabel Pra\c{c}a

benchmarks

arXiv Machine Learning

Jul 2

Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance

arXiv:2607. 00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing.

By Vinicius Herique Kieling, Guilherme Macedo Baggio, Felipe Augusto Bueno Rossi, Marco Antonio de Castro Barbosa, Dalcimar Casanova, Larissa Macedo dos Santos Tonial, Jefferson Tales Oliva

arXiv Machine Learning

Jul 30

TabPFN Extensions for Interpretable Geotechnical Modelling

arXiv:2603. 21033v3 Announce Type: replace-cross Abstract: Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where uncertainty quantification and interpretability matter as much as predictive accuracy.

By Taiga Saito, Yu Otake, Daijiro Mizutani, Stephen Wu

rag benchmarks safety

arXiv Machine Learning

Jul 8

SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR

arXiv:2607. 05484v1 Announce Type: cross Abstract: The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability to explain model outputs in a manner auditable by supervisory bodies.

By Ujjwala Vadrevu

safety

arXiv Machine Learning

1d ago

In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults

arXiv:2608. 14663v1 Announce Type: new Abstract: As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers that discourage physical activity and social participation among older adults.

By Houhao Liang, Kresimir Friganovic, Joanne Kua, Noor Hafizah Ismail, Su Su, Bryan Yijia Tan, Navrag B. Singh, Panos Mavros

llms

arXiv AI

Jul 21

Short-Term Electricity Demand Forecasting for New England: A Comprehensive Machine Learning Benchmark with Weather, Calendar, and COVID-19 Indicators

arXiv:2606. 20918v2 Announce Type: replace-cross Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimization.

By Reza Ghanavati, Behrooz Mosallaei

llms benchmarks

arXiv Machine Learning

Aug 11

Full-Feature versus Limited-Input Machine Learning for Residential Energy Estimation: A Comparative Analysis of RECS and ResStock Under Realistic Input Constraints

arXiv:2608. 09255v1 Announce Type: new Abstract: Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available.

By Aditya Ramnarayan, Fatih Evren, Patti Gunderson, Samuel Rosenberg

benchmarks

arXiv AI

Jul 21

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair

benchmarks

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