An accurate pavement performance forecasting model is essential for transportation agencies to perform pavement maintenance, rehabilitation, and reconstruction (MR&R) in a predictive and cost-effective manner. Although some forecasting methods have been successful in forecasting short-term (e.g., 1–2 year) pavement conditions at either the project level or network level, accurately forecasting long-term (e.g., 3–5 year) pavement conditions at both project level and network level under real-world conditions is still challenging. Thus, the goal of this paper is to propose a two-stage machine learning approach based on long short-term memory (LSTM) to achieve not only the short-term, but also the long-term, forecasting accuracy at both the project level and network level. The proposed method involves LSTM in the first stage and an artificial neural network (ANN) in the second stage, resulting into a two-stage model. The LSTM first learns the pattern of pavement deterioration based on sequential data (e.g., historical pavement conditions). Then, the ANN further learns the impacts of roadway factors (e.g., traffic parameter, pavement surface type, working district) to adjust the final forecasting results. The accuracy of the proposed two-stage model has been compared with baseline machine learning methods in 2016 on a large, statewide Florida dataset at both the project level and network level to demonstrate the superior capability of the proposed method. In addition, the proposed method has been tested further to forecast future (5-year) pavement conditions (2016–2020). Results show a promising forecasting accuracy for both the short-term and long-term in comparison with the ground truth.

We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field work. To effectively accommodate the weak informativeness of ambient data, a selective data sampling scheme and an attention-based deep multiple instance classification model are adopted, which only requires weakly annotated data. The proposed approach is validated on field data collected by a fiber sensing system over multiple existing fiber networks.