Deep learning-based pavement performance modeling using multiple distress indicators and road work history

• URL: http://arxiv.org/abs/2605.01914v1
• Date: Sun, 03 May 2026 14:51:20 GMT
• Title: Deep learning-based pavement performance modeling using multiple distress indicators and road work history
• Abstract summary: This paper uses deep neural networks such as the convolutional neural network (CNN) and the long short-term memory (LSTM) to model the pavement deterioration process.<n>Twenty-one flexible pavement condition indicators, including cracking, rutting, raveling, and roughness, collected from more than 100,000 pavement sections were included in the proposed models.<n>Case study results show that the proposed CNN model outperforms standard machine learning models in predicting pavement condition values.
• Score: 2.2351162816271386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other unobserved variables. Accurate predictions of pavement condition can help maximize the use of available resources for pavement management agencies through better coordinated preservation and maintenance activities. This paper uses deep neural networks such as the convolutional neural network (CNN) and the long short-term memory (LSTM) to model the pavement deterioration process. In this paper, pavement condition data and maintenance and rehabilitation history collected by the Texas Department of Transportation over the past 18 years were used. Twenty-one flexible pavement condition indicators, including cracking, rutting, raveling, and roughness, collected from more than 100,000 pavement sections were included in the proposed models. Promising preliminary results were obtained. Case study results show that the proposed CNN model outperforms standard machine learning models in predicting pavement condition values.

Related papers

• On Predicting the Post-training Potential of Pre-trained LLMs [60.07459271263409]
  We introduce a new task of predicting post-training potential - forecasting a base model's performance before post-training.<n>We propose RuDE, a unified framework that bypasses the generation gap of base models by leveraging response discrimination.<n>Experiments demonstrate a correlation greater than 90% with post-training performance.
  arXiv  Detail & Related papers  (2026-05-12T11:33:49Z)
• Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels [72.3670919950349]
  Large language models (LLMs) acquire substantial world knowledge during pre-training.<n>Post-training techniques such as supervised fine-tuning (SFT) shape this knowledge change behavior.<n>We evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLaMA-3 families.
  arXiv  Detail & Related papers  (2025-09-20T09:40:32Z)
• Considering Spatial Structure of the Road Network in Pavement Deterioration Modeling [33.54970710607668]
  This research incorporated spatial dependence of road network into pavement deterioration modeling through a graph neural network (GNN)<n>The data used in this research comprises a large pavement condition data set with more than a half million observations taken from the Pavement Management Information System (PMIS) maintained by the Texas Department of Transportation.
  arXiv  Detail & Related papers  (2025-08-02T23:48:53Z)
• Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy [104.48511402784763]
  Performance Law for SR models aims to theoretically investigate and model the relationship between model performance and data quality.<n>We propose Approximate Entropy (ApEn) to assess data quality, presenting a more nuanced approach compared to traditional data quantity metrics.
  arXiv  Detail & Related papers  (2024-11-30T10:56:30Z)
• LaCo: Large Language Model Pruning via Layer Collapse [56.92068213969036]
  Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion. Existing methods such as model quantization, knowledge distillation, and model pruning are constrained by various issues. We propose a concise layer-wise structured pruner called textitLayer Collapse (LaCo), in which rear model layers collapse into a prior layer.
  arXiv  Detail & Related papers  (2024-02-17T04:16:30Z)
• Fine-tuning vision foundation model for crack segmentation in civil infrastructures [13.731957127190274]
  Fine-tuning methods are adopted to fine-tune the foundation model in semantic segmentation: the Segment Anything Model (SAM) CrackSAM exhibits remarkable superiority, particularly under challenging conditions such as dim lighting, shadows, road markings, construction joints, and other interference factors. These cross-scenario results demonstrate the outstanding zero-shot capability of foundation models and provide new ideas for developing vision models in civil engineering.
  arXiv  Detail & Related papers  (2023-12-07T11:39:11Z)
• A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia [0.0]
  Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides. High-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, but their computational complexity renders them unsuitable for real-time applications. This study compares the performance of a surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
  arXiv  Detail & Related papers  (2023-07-26T13:24:01Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Churn Reduction via Distillation [54.5952282395487]
  We show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines.
  arXiv  Detail & Related papers  (2021-06-04T18:03:31Z)
• Learning representations with end-to-end models for improved remaining useful life prognostics [64.80885001058572]
  The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. We propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. We will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T16:45:18Z)
• Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis [2.752817022620644]
  This study deploys state-of-the-art deep learning algorithms to detect and characterize pavement distresses. The models were trained using 21,041 images captured across urban and rural streets of Japan, Czech Republic and India. The best performing model achieved an F1 score of 0.58 and 0.57 on two test datasets released by the IEEE Global Road Damage Detection Challenge.
  arXiv  Detail & Related papers  (2020-10-21T00:26:59Z)
• Deep Machine Learning Approach to Develop a New Asphalt Pavement Condition Index [0.0]
  In recent years, advancement in deep learning has enabled researchers to develop robust tools for analyzing pavement images at unprecedented accuracies. Deep learning models necessitate a big ground truth dataset, which is often not readily accessible for pavement field. In this study, we reviewed our previous study, which a labeled pavement dataset was presented as the first step towards a more robust, easy-to-deploy pavement condition assessment system.
  arXiv  Detail & Related papers  (2020-04-28T05:57:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.