Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines

While machine learning (ML) provides a powerful tool for correlating various factors and asphalt pavement cracking, its poor interpretability hinders a deeper mechanistic analysis of how these factors contribute to crack development. In this regard, this study proposed an interpretation framework for ML models, which consists of feature importance based on the SHapley Additive exPlanations (SHAP) method, factor influence analysis based on Accumulated Local Effects (ALE) and local conditional effects (LCE), and feature interaction analysis based on H-statistic and second-order ALE. Using the U.S. long-term pavement performance (LTPP) database, a gradient boosting regression tree (GBR) model was developed with maintenance, design, traffic, and climate factors as inputs and transverse, longitudinal, and alligator cracking as outputs. The generalization performance of all three cracking models exceeded 82%. Results indicated that transverse cracking is primarily triggered by low-temperature events, while longitudinal and alligator cracking are mainly caused by fatigue damage induced by traffic loads. Feature interaction analysis further revealed significant interactions in transverse cracking between the cumulative freezing index (CFI) and traffic load (CESAL) and in fatigue cracking between freeze‒thaw cycles (CFT) and precipitation (CTP). The outcome of this study is expected to bridge the gap between data-driven predictions and mechanistic understanding, providing interpretable insights for the design of durable asphalt pavements and the optimization of maintenance strategies.

This article discusses the construction and performance of Continuously Reinforced Concrete Pavements (CRCP) reinforced with Flexarm, a novel steel reinforcement developed in France to address CRCP construction challenges. Initially, CRCP's historical background in the U.S. and Europe is explored, noting its established use in the U.S. for heavy highway loadings and its later adoption in Europe. The article then introduces the development of Flexarm in the mid-1980s as a solution to logistical and labor-intensive challenges posed by traditional CRCP construction methods. Flexarm's design, characterized by its flat ribbon shape, high yield stress, and ease of installation, aimed at reducing the effective work zone and labor at construction sites. The paper presents a comprehensive field evaluation of CRCP pavements in France using conventional and Flexarm reinforcement. This evaluation includes observations from construction sites and performance analyses based on crack spacing, crack width, and overall pavement condition. The findings suggest that while there are differences in crack spacing and width between pavements reinforced with conventional steel and those with Flexarm, further research is necessary to understand these differences' significance fully. The article highlights the efficiency and potential benefits of using Flexarm, including a reduced work zone, lower labor requirements, and improved safety. It suggests that Flexarm's high strength, corrosion resistance, and cost competitiveness make it an appealing option for CRCP construction in new projects, reconstructions, and unbonded overlays. Concluding, the article calls for additional research to validate the long-term performance of Flexarm-reinforced pavements and to develop an acceptable design methodology that accounts for its unique properties. The outcome of such research could have significant implications for the design and construction of CRCP, potentially influencing practices in the U.S. and elsewhere. (Abstract generated by AI tool ChatGPT 4)

Buckling prediction based on joint opening and safe temperature estimation in jointed plain concrete pavements

Imporved model for pavement performance prediction based on recurrent neural network using LTPP database

Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide.

This study focuses on the critical aspect of skid resistance in asphalt pavements, an essential factor for ensuring road safety. Several factors, including pavement texture, aggregate properties, and environmental conditions, influence skid resistance. Utilizing data from the Long-Term Pavement Performance (LTPP) database, this research develops an Artificial Neural Network (ANN) model to predict skid resistance based on a comprehensive array of inputs such as traffic volume, pavement age, asphalt content, and environmental factors. A key outcome of this study is the derivation of an explicit algebraic equation that fully represents the trained ANN model, allowing practitioners to compute Friction Number (FN) without running the whole model. The model was trained with data involving 135 GPS pavement sections across various climate zones in the United States, achieving a robust Coefficient of Determination (R²) of 0.87, a Root Mean Square Error (RMSE) of 2.52, a Mean Absolute Error (MAE) of 2.04, and a Mean Absolute Percentage Error (MAPE) of 4.4%. The findings underscore the significant impact of traffic loads, pavement age, and environmental conditions on skid resistance. The study’s methodology and findings contribute to the broader understanding of pavement management and safety strategies, providing a valuable tool for predicting and maintaining optimal skid resistance in asphalt pavements. Recommendations for future research include expanding the dataset and refining the model with additional influential factors to enhance prediction accuracy and reliability.

Performance management is increasingly emphasized at the state and national levels. This is evident in the Moving Ahead for Progress in the 21st Century Act (MAP-21) and performance targets set by individual state Departments of Transportation (DOTs). This management approach requires the establishment of performance goals, measures, and metrics for pavement networks, and systematic measurement of progress towards achieving the goals. This paper evaluates the performance of continuously reinforced concrete pavement (CRCP) in the Houston, Texas area based on the performance management criteria outlined in MAP-21 and the Texas Department of Transportation (TxDOT) statewide pavement performance goal. The performance metrics used in this analysis are the International Roughness Index (IRI), punchouts per mile, and TxDOT’s Condition Score (CS). The data for conducting this analysis was obtained from TxDOT’s Pavement Management Information System (PMIS) database. Despite approaching or exceeding the end of its design life, the performance of CRCP in the Houston District has been consistently successful over the past nine years based on the performance management criteria outlined in MAP-21 and TxDOT’s statewide pavement performance target.

The performance of rigid pavement is greatly affected by the properties of the base/subbase and subgrade layer. However, the performance predicted by the AASHTOWare Pavement Mechanistic-Empirical (ME) Design shows low sensitivity to the properties of base and subgrade layers. In this study, a new set of modified models, that is, resilient modulus ( MR) and modulus of subgrade reaction ( k-value) are adopted to better reflect these layers’ influence on the overall performance. An artificial neural network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation data sets in the ANN model consist of 27,000 simulation cases with different combinations of pavement layer thickness, layer modulus, and slab-base interface bond ratio. Eight pavement section data are collected from the long-term pavement performance (LTPP) database and modeled using the FE software ISLAB2000 to evaluate the sensitivity of the modified MR model and k-values to pavement performance. The computational results indicate that the modified MR values have a higher sensitivity to water content in the base layer on fatigue cracking (bottom-up and top-down cracking) and the faulting performance of rigid pavements compared with the results using the Pavement ME design model. It is also observed that the k-values using the ANN model have a greater sensitivity to cracking and faulting performance as a result of changes in any bonded conditions (fully bonded, partially bonded, zero bonded), whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonded and zero bonded).

Timely completion of construction projects is vital for cost control, stakeholder satisfaction, and project success. In India, however, schedule adherence is frequently challenged by factors such as resource limitations, weather disruptions, and design changes. This study presents an AI-driven predictive modeling framework to forecast schedule performance using real-world data from Indian construction projects. Five machine learning models—multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANN), random forest regressor (RFR), and gradient boosting machine (GBM)—were developed and evaluated. Models were categorized as white box, grey box, or black box based on interpretability. MLR, while transparent, showed limited performance in complex scenarios. SVR and RFR offered a balance between accuracy and explainability, whereas ANN and GBM achieved the highest predictive accuracy. GBM emerged as the top performer (R² = 0.93). Feature importance and sensitivity analyses identified planned duration, workforce size, equipment utilization, and material availability as key drivers of schedule adherence. The findings provide actionable insights for selecting suitable AI models in construction planning and underscore the value of integrating predictive analytics into schedule risk management for Indian infrastructure projects.

Robot arms are machines which are not only used in industrial technologies but also other applications such as medicine and agriculture. The robot arm movement control is important in the use of robot arms. This paper presents model-based reinforcement learning (MBRL) in robot arm movement control in case studies where targets are in random positions. The numerical studies of 2 and 3-DOF robots moving in planar motion are used as test problems. In 2-DOF robot control, 2 tasks - placing and reaching are employed. In 3-DOF robot control, noise is considered. Machine learning regression techniques - Gaussian process regression (GPR), artificial neural network (ANN), and support vector regression (SVR) - are used in environment modelling in MBRL while particle swarm optimization (PSO) is used as optimization tool in MBRL. The numerical studies show that MBRL with GPR has the highest performance with at least 95% success rate while MBRL with the other regression techniques such as ANN and SVR can succeed only 23-93% and 31-47%, respectively. Therefore, MBRL with GPR and PSO is suitable for robot arm movement control.

With the increasing global warming and enormous pollution, it is very obvious to generate power from renewable energy sources. In fact, photo voltaic (PV) power is a main renewable energy source for sustainable power generation. But PV power generation is uncertain and intermittent in nature. Stable and reliable power supply may not be feasible with PV power. Power supply reliability is important as much as its availability. To handle the inconsistency of PV power generation, power sector is highly dependent on forecasting methods and techniques. In this paper the feed forward neural network (FFNN) of artificial neural networks(ANN) with optimization technique particle swarm optimization (PSO) and support vector regression (SVR) are used in short term PV power generation forecasting and their performance is compared with the error calculation in terms of the mean absolute error (MAE) and root mean squared error (RMSE). The PV power and meteorological data of solar irradiation and temperature of Kolkata region of India was used for forecasting. Similar days of the year sunny days, cloudy days and rainy days are clustered using k-means clustering technique. Thereafter, a feed forward neural network is implemented to forecast a day head PV power and week ahead photo voltaic power. PSO is used to optimize the weights of the neural network. The performance of the proposed forecasting method is compared to a data mining approach, support vector regression (SVR).In this paper, the significance of proper selection of input parameters in PV power forecasting is emphasized. With the application of K-means clustering, the accuracy of ANN-PSO approach is improved significantly in a day ahead PV power forecasting and a week ahead PV power forecasting with a good margin. Even the accuracy of ANN-PSO approach after K-means clustering is better than SVR model in a week ahead forecasting

Pavement friction data from the Long-Term Pavement Performance (LTPP) database were treated and analyzed in order to examine its quality, patterns, and potential use. Data from a number of ...

A statistical property of non-stationary time series is applied to quantify the long-run equilibrium relationship between mechanisms of pavement failure and deterioration. The concept is applied to measured pavement responses from an Accelerated Pavement Test (APT) conducted on the thin (185 mm) Continuously Reinforced Concrete Pavement (CRCP) of the Ben Schoeman freeway in South Africa. The statistical property is that of cointegration, which allows testing for the existence of equilibrium relationships among trending variables within fullydynamic specification frameworks. The predominant failure mechanisms leading to punchout are known to be loss of load transfer capability at transverse cracks and the deterioration of substructure support. The long-term relationship between proxy variables for the mechanisms leading to punchout and pavement deterioration is determined. The average long-term relationship between a change in relative movement and a change in surface deflection at transverse cracks is found to be 17.5. This means that on average a 1.0 % increase in relative movement will translate into a 17.5 % increase in surface deflection and vice versa. The empirical quantification of the long-term equilibrium relationships between pavement mechanisms of failure and deterioration offers the potential to improve the reliability of design systems and may also be used in pavement management.

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques. This study employed Gradient Boosting (GB) to evaluate and predict the CS of hollow masonry prism. The database consists of 102 hollow concrete specimens taken from different previous published literature used for modeling. The output is the CS of the hollow masonry prism, while the inputs include the compressive strength of mortar (fm), the compressive strength of blocks (fb), height-to-thickness ratio (h/t), the ratio of fm/fb. To reduce the overfitting problem, this study used K-Fold cross-validation, then particle swarm optimization (PSO) was employed to obtain the optimum hyperparameter. The GB model then was modeled using the optimum hyperparameters. The results showed that the GB model performed very well in evaluating and predicting the CS of the hollow masonry prims with a high prediction accuracy, the values of R2, RMSE, MAE, and MAPE are 0.977, 0.803 MPa, 0.612 MPa, and 0.036%, respectively. The performance of the GB model in this study outperformed in comparison to six different machine learning models (decision tree, linear regression, random forest regression, ridge regression, Artificial Neural network, and Extreme Gradient Boosting) used in previous studies. The results of sensitivity analysis using SHAP and PDP-2D indicate that the CS is strongly dependent on the fb (with a mean SHAP value of 3.2), h/t (with a mean SHAP value of 1.63), while the fm/fb (with a mean SHAP value of 0.57) had a small effect on the CS. Thus, it can be stated that this research provides a good method to evaluate and predict the CS of the hollow masonry prism, which can bring good knowledge for practical application in this field.