A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements

Kaloop, Mosbeh R.; El-Badawy, Sherif M.; Ahn, Jungkyu; Sim, Hyoung-Bo; Hu, Jong Wan; El-Hakim, Ragaa T. Abd

doi:10.1080/10298436.2020.1776281

Cited by 36 publications

(10 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the ELM, the OP-ELM enhanced the robustness and accuracy of the network. However, it had a higher computational time, affecting the accuracy and training time [ 6 , 7 ]. Genetic algorithms for pruned ELM (GPA-ELM) were proposed by Alencar et al [ 8 ] to prune the hidden layer neurons based on multiobjective GAs.…”

Section: Related Workmentioning

confidence: 99%

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

Barioul

Kanoun

2023

Sensors

View full text Add to dashboard Cite

The recognition of hand signs is essential for several applications. Due to the variation of possible signals and the complexity of sensor-based systems for hand gesture recognition, a new artificial neural network algorithm providing high accuracy with a reduced architecture and automatic feature selection is needed. In this paper, a novel classification method based on an extreme learning machine (ELM), supported by an improved grasshopper optimization algorithm (GOA) as a core for a weight-pruning process, is proposed. The k-tournament grasshopper optimization algorithm was implemented to select and prune the ELM weights resulting in the proposed k-tournament grasshopper extreme learner (KTGEL) classifier. Myographic methods, such as force myography (FMG), deliver interesting signals that can build the basis for hand sign recognition. FMG was investigated to limit the number of sensors at suitable positions and provide adequate signal processing algorithms for perspective implementation in wearable embedded systems. Based on the proposed KTGEL, the number of sensors and the effect of the number of subjects was investigated in the first stage. It was shown that by increasing the number of subjects participating in the data collection, eight was the minimal number of sensors needed to result in acceptable sign recognition performance. Moreover, implemented with 3000 hidden nodes, after the feature selection wrapper, the ELM had both a microaverage precision and a microaverage sensitivity of 97% for the recognition of a set of gestures, including a middle ambiguity level. The KTGEL reduced the hidden nodes to only 1000, reaching the same total sensitivity with a reduced total precision of only 1% without needing an additional feature selection method.

show abstract

Section: Related Workmentioning

confidence: 99%

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

Barioul

Kanoun

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The models were developed using the training dataset, while the testing data was utilized for performance assessment of the proposed models. For the deep neural network Source: Modified from (18) modeling, the data were categorized randomly into training (70%), validation (15%), and testing (15%) datasets.…”

Section: Data Collection and Processingmentioning

confidence: 99%

“…The results produced an estimated error for the developed ANN model approaching only 1% for the pavements located in wet no-freeze climate zones in the US. Kaloop et al ( 18 ) developed a hybrid Wavelet Optimally Pruned Extreme Learning Machine (WOPELM) model to estimate the IRI of rigid pavements using eight input variables: IRI 0 , pavement age, percent joints spalled, flexible and rigid patching areas, transverse cracks, FI, P200, and total joint faulting. Their proposed model predictions showed an error of 7%.…”

mentioning

confidence: 99%

“… Note : AADTT = Annual Average Daily Truck Traffic; AAFI = Annual Average Freezing Index; AAMaH = Annual Average Maximum Humidity; AAMiH = Annual Average Minimum Humidity; AAP = Annual Average Precipitation; ANN = Artificial Neural Network; CN = Construction Number; CRCP = Continuous Reinforced Concrete Pavement; CS&O = Crack Sealing and Overlay on JPCP; ESALs = Equivalent Single Axle Loads; FI = Freezing Index; IRI0 = Initial IRI; HAC = Depth of asphalt overlay; HPCC = Thickness of the concrete slab; KESALs = Annual ESALs in millions; JPCP = Jointed Plain Concrete Pavement; MAAT = Mean Annual Air Temperature; MAPE = Mean Absolute Percentage Error; MARE = Mean Absolute Relative Error; MLR = Multilinear Regression; N = Number of data points; NA = Not Available; N F&Th = Number of freeze/thaw cycles; P200 = Fines percentage passing No. 200 US sieve; PATCH = Flexible and rigid patching; PO = Number of punchouts; SEE = Standard Error of the Estimate; SPALL = Joints with spalling; RMSE = Root Mean Square Error; R 2 = Coefficient of determination; Se/Sy = Ratio of standard error of estimate to standard deviation of the measured values; TC = Transverse Cracking; TFAULT = Total joint faulting; VA = Air Voids; w/c = Water cement ratio; WOPELM = Wavelet Optimally Pruned Extreme Learning Machine; σ PCC = Plain concrete compressive strength. Source : Modified from ( 18 ) …”

mentioning

confidence: 99%

See 1 more Smart Citation

International Roughness Index Prediction for Jointed Plain Concrete Pavements Using Regression and Machine Learning Techniques

Suliman

Awed

El-Hakim

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

Rigid pavements are recently gaining vast interest for their durability and large traffic volume endurance. This raises the need for a robust method to predict their long-term performance to support decisions on their future maintenance and rehabilitation requirements. One of the most descriptive parameters of pavement ride quality is the International Roughness Index (IRI). IRI can be used as a performance indicator for the level of serviceability of rigid pavement. This study presents the evaluation of 21 variables covering climate, traffic, and structural parameters in predicting IRI for jointed plain concrete pavement (JPCP) sections using 1,414 data points acquired from the Long-Term Pavement Performance (LTPP) database. It was found that only 10 variables are significant in the IRI prediction for JPCP. Moreover, four modeling techniques were applied, namely, linear regression, multivariate adaptive regression splines, Gaussian process regression, and artificial neural network. It was found that a deep ANN model with a structure of one input layer, three hidden layers, and one output layer [10-36-18-9-1] gives the highest IRI prediction accuracy with root mean square error and coefficient of determination (R2) of 0.117 and 0.92, respectively. Finally, a sensitivity analysis was performed to evaluate the impact of each input variable on the accuracy of IRI prediction for JPCP sections, and it was found that the most influential variables are initial IRI, pavement age, mean and standard deviation of humidity, standard deviation of evaporation, mean and standard deviation of freezing index, pavement compressive strength, ratio between pavement and base layers thickness, and finally an estimated parameter called site factor which is a function of (age, freezing index and soil percentage passing sieve no. 200).

show abstract

“…A recent study of such work and related references can be found in the work by Abdelaziz et al [22]. Kaloop et al [23] integrated Optimally Pruned Extreme Learning Machine (OP-ELM) and Wavelet analysis to improve the OP-ELM results and designed a novel hybrid Wavelet-OPELM (WOPELM) model for predicting International Roughness Index (IRI). Guo et al [24] proposed an ensemble learning model that utilized a Gradient Boosting Decision Tree (GBDT) to predict IRI and rut depth.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Pavement Structural Condition Using Machine Learning Methods

et al. 2022

View full text Add to dashboard Cite

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

show abstract

A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements

Cited by 36 publications

References 60 publications

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

International Roughness Index Prediction for Jointed Plain Concrete Pavements Using Regression and Machine Learning Techniques

Predicting Pavement Structural Condition Using Machine Learning Methods

Contact Info

Product

Resources

About