Simulating pavement structural condition using artificial neural networks

This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.     

Stiffness characterisation of full-scale airfield test pavements using computational intelligence techniques

The falling weight deflectometer (FWD) is a non-destructive test equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. The backcalculated moduli are not only good pavement layer condition indicators but are also necessary inputs for conducting mechanistic based pavement structural analysis. In this study, artificial neural networks (ANNs)-based backcalculation models were employed to rapidly and accurately predict flexible airport pavement layer moduli from realistic FWD deflection basins acquired at the U.S. Federal Aviation Administration's National Airport Pavement Test Facility (NAPTF). The uniformity characteristics of NAPTF flexible pavements were successfully mapped using the ANN predictions.     

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R²≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R² = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.     

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R² = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.     

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

In this study, we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System (ANFIS) optimized by Shuffled Complex Evolution (SCE) on the one hand and ANFIS with Artificial Bee Colony (ABC) on the other hand. These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory. Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway, Vietnam were considered. The dataset was randomly divided into a 70:30 ratio, for training (70%) and testing (30%) of the hybrid models. Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that both of the novel models depict close agreement between experimental and predicted results. However, the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs. Thus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.     

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

The consolidation coefficient of soil (C_v) is a crucial parameter used for the design of structures leaned on soft soi. In general, the C_v is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine C_v via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the C_v of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R² > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy ( R² = 0.9869), the difference compared with other models was negligible (i.e., R² = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict C_v based on the basic soil parameters.     

Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Abstract:  Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro-fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. These parameters are generally obtained by performing falling weight deflectometer tests and monitoring the development of distresses on the pavement surface. The proposed hybrid model for predicting pavement performance was characterized by multilayer, feedforward neural networks that led the reasoning process of the IF-THEN fuzzy rules. The results of the neuro-fuzzy model were superior to those of the linear regression model in terms of accuracy in the approximation. The proposed neuro-fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques .     

Fuzzy logic modeling of deflection behavior against dynamic loading in flexible pavements

Flexible pavements are especially affected by moving vehicles. As a result of the moving vehicles, the pavement starts to deteriorate. For the determination of the structural capacity of the pavement, non-destructive testing equipments are used. These are mainly Benkelman beam, Dynaflect and falling weight deflectometer (FWD). In such a process, the most important thing is to analyze the collected data. In general, linear elastic theory and finite element method are used for this purpose. Since linear elastic theory and finite element method are time consuming, a fuzzy logic approach is used for the elimination of this drawback during the course of this study. Results indicate that the fuzzy logic approach can be used for the modeling of the deflection behavior against dynamic vehicle loading for flexible pavements. The fuzzy model is able to predict the deflection behavior against dynamical loading. The new approach can capture the non-linearity of surface deflection behavior.     

Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.     

Evaluation and prediction of slope stability using machine learning approaches

In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R ², MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.     

Pavement sustainability index for highway infrastructures: A case study of Maryland

Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R²) is correlated and significant, R² = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value= 0.028, and international roughness index (IRI) coefficient value= ?0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values.     

Modeling and validation of impact forces for back-calculation of pavement surface moduli

When an impact load is sufficiently small, its influence on the pavement structure is mainly from the surface layer material. To explore the influence depth of an impact load and back-calculation of the pavement surface modulus, both numerical calculation and experimental testing were conducted, and the results are presented in this paper. The numerical calculation was performed through a DEM-FDM-coupled model. After a new modulus back-calculation algorithm is formed by analyzing the numerical modeling results, experimental tests were also conducted for verification, and the results were analyzed. The max value of the falling weight impact force was closely related to the elastic modulus of the material, and the influence depth was controllable. Finally, it is proved that the method can be used to calculate the surface layer modulus and estimate the surface layer thickness.     

A hybrid machine learning model to estimate self-compacting concrete compressive strength

This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.     

SPT based determination of undrained shear strength: Regression models and machine learning

The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like p-value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error (RMSE) values and coefficient of determination (R²). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.     

Co-variance matrix adaptation evolution strategy for pavement backcalculation

The falling weight deflectometer (FWD) is the foremost and widely accepted tool for characterizing the deflection basins of pavements in a non-destructive manner. The FWD pavement deflection data are used to determine the in situ mechanical properties (elastic moduli) of the pavement layers through inverse analysis, a process commonly referred to as backcalculation (B/C). Several B/C methodologies have been proposed over the years, each with individual strengths and weaknesses. Hybrid methods (combining two methods or more) are recently proposed for overcoming problems posed by stand-alone methods, while extracting and compounding the benefits that are individually offered. This paper proposes a novel hybrid strategy that integrates co-variance matrix Adaptation (CMA) evolution strategy, Finite element (FE) modeling with neural networks (NN) non-linear mapping for backcalculation of non-linear, stress dependent pavement layer moduli. The resulting strategy, referred as CMANIA (CMA with neural networks for inverse analysis) is applied for asphalt pavement moduli backcalculation and is compared with a conventional B/C approach. Results demonstrate the superiority of this method in terms of higher accuracy, achieving nearer to global solutions, better computational speed, and robustness in predicting the pavement layer moduli over the conventional methods.     

沥青路面动态黏弹反演研究

对沥青路面结构进行动态黏弹反演, 正向分析采用谱单元法,采用典型路面结构分析验证了谱单元法动态黏弹分析的正确性;采用实测和计算得到的路表弯沉整体时程曲线的均方误差作为反演误差控制方程,以序列二次规划算法作为优化算法,对路面结构层力学参数进行优化。对半刚性基层和复合式基层两种沥青路面结构动态进行黏弹反演,结果表明：利用谱单元法对沥青路面进行动态黏弹响应分析,可以极大地提高计算效率,动态反演可充分利用弯沉时程曲线的数据信息,反演得到的动态模量、相位角数主曲线组成完整的黏弹力学参数,可以更全面地描述沥青层的力学特性以及荷载作用频率和温度场的影响。动态黏弹反演方法可以弥补传统反演结果无法得到的力学参数主曲线的缺陷,为沥青路面分析及质量评定提供了有效的方法。     

PFWD在某高速公路施工质量监测中的应用

依托江门至肇庆高速公路项目,介绍便携式落锤弯沉仪( PFWD)在施工质量控制中的应用,通过对该高速公路各结构层强度进行检测,得到回弹模量分布,并根据检测数据着重分析了高等级沥青路面施工控制要点.     

山东某机场道面的测试评定

本文介绍了山东省某机场道面测试评定的方法和内容.同时还介绍了将雷达探测应用于机场道面基础的密实度和均匀性测试.测试结果表明,该机场道面面层需加厚;采用探地雷达进行机场道面基础探测是可行的,建议在测试评定标准中增加该项内容.     

雷达检测技术在市政工程中的应用

基于意大利IDS公司的博泰克RIS探地雷达的雷达检测技术,可广泛地应用于隧道检测、管线探测、路基检测、沥青铺设质量评估等市政工程的检测。该技术的成功推广应用,为大量的市政隐蔽工程的质量检测、管理维护提供了一种高效准确的无损检测手段。     

山东某机场道面的测试评定

本文介绍了山东省某机场道面测试评定的方法和内容。同时还介绍了将雷达探测应用于机场道面基础的密实度和均匀性测试。测试结果表明,该机场道面面层需加厚;采用探地雷达进行机场道面基础探测是可行的,建议在测试评定标准中增加该项内容。