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This paper represents the results of a comprehensive experimental work on developing high performance concrete mixes containing different percentages of electric arc furnace steel slag (EAFSS) coarse aggregate (0, 25, 50, 75, and 100% by weight) for sustainable pavements. The use of steel slag powder (SSP) and silica fume (SF) as a mineral filler was also investigated. Mechanical properties of concrete such as compressive, splitting tensile and flexural strengths, modulus of elasticity, permeability, and thermal conductivity, were measured in the laboratory. The morphological and microstructural properties of the investigated steel slag were also determined. Finally, the field performance of the investigated concrete mixes was predicted using the AASHTOWare Pavement ME for a typical Jointed Plain Concrete Pavement (JPCP) system in terms of transverse cracking, faulting, and roughness. The results of this research indicate that, all investigated mechanical properties for steel slag mixes are better than the natural aggregate mix with optimum mechanical properties and performance for the concrete mix with 50% EAFSS. Using the SSP powder with or instead of SF did not show significant improvement in mechanical properties/performance. The results emphasise that EAFSS is a viable sustainable construction material for rigid pavements. [ABSTRACT FROM AUTHOR]

This study presents a novel hybrid intelligent approach using Extreme Learning Machine (ELM) and Equilibrium Optimiser (EO) (ELM-EO) for predicting resilient modulus, Mr of Unbound Granular Materials (UGMs). Fourteen various blends of Recycled Concrete Aggregate (RCA) with Recycled Clay Masonry (RCM), and Electric Arc Furnace Steel (EAFS) slag with limestone aggregates were tested in the laboratory using routine and advanced tests. The laboratory Mr testing produced 224 measurements based on the average of triplicate samples for each blend. The performance of the ELM-EO approach was evaluated and compared with conventional regression, ELM-biogeography-based optimisation (BBO) (ELM-BBO) and ELM-genetic algorithm (ELM-GA) approaches using the same input properties. The inputs used for the Mr prediction are the bulk stress, percent of RCM, and/or percent of EAFS. The results demonstrate that the performance of ELM-EO and ELM-BBO approaches is better than ELM-GA and regression approaches for predicting Mr. The overall statistical measures of the proposed approaches show that the ELM-EO approach ranks first as it outperforms the other approaches with coefficient of determination (R2) of 0.924 and Root Mean Square Error (RMSE) of 37.08 MPa. [ABSTRACT FROM AUTHOR]

International Roughness Index (IRI) is a key parameter in pavement performance evaluation. This study investigates developing a reliable prediction model that can be used to estimate IRI of rigid pavements using innovative machine learning techniques. Optimally Pruned Extreme Learning Machine (OP-ELM) and Wavelet analysis are integrated to improve the OP-ELM results and design a novel hybrid Wavelet-OPELM (WOPELM) model for the IRI prediction. The proposed model is compared statistically to the OP-ELM and conventional feed-forward Artificial Neural Network (ANN) as well as regression model with respect to their efficiency to predict IRI of jointed plain concrete pavement (JPCP) sections in USA. The relevant data was collected from the Long-Term Pavement Performance (LTPP) database. Eight input variables, initial IRI, pavement age, transverse cracks, percent joints spalled, flexible and rigid patching areas, total joint faulting, freeze index, and percent subgrade passing No. 200 U.S. sieve, are assessed and used to predict the IRI. The results show that the initial IRI, total joint faulting, and freezing index are the most significant parameters for IRI prediction. The WOPELM is found to be a robust and more accurate modelling technique compared to OP-ELM, ANN, and regression for IRI prediction with only a 7% prediction error. [ABSTRACT FROM AUTHOR]

Currently, Egypt is using the [AASHTO, 1993. Guide for design of pavement structures. Washington, DC: American Association of State Highway and Transportation Officials] guide for the flexible pavement structural design. In this method the resilient modulus (MR) of the unbound granular base/subbase materials and subgrade soils is a measure of material stiffness used for characterisation. It is considered one of the major factors affecting the pavement layerthicknesses. However, in Egypt, California Bearing Ratio (CBR) is the main strength parameter used for characterisation of the unbound materials and subgrade soils due to its availability and simplicity. In this research, a comprehensive experimental testing programme was conducted on different unbound granular base/subbase materials typically used in pavement construction projects in Egypt. The testing programme includes MR, CBR, and basic material properties. The effect of aggregate gradation on MR is investigated. A model predicting MR as a function of the basic material properties is proposed. The MR–CBR relationship adopted in the current Egyptian Code of Practice is verified based on the testing results. The experimental data warranted the calibration of the current model. The calibrated model has reasonable goodness of fit statistics and yields rational MR estimates for the local materials. [ABSTRACT FROM AUTHOR]

International Roughness Index (IRI) and Pavement Condition Index (PCI) are among other pavement condition indices used to assess pavement surface condition. The literature suggests that most of the pavement indices are related as a result of which several models have been developed to predict one index from the other. This study uses the Long-Term Pavement Performance (LTPP) database to develop a simplified regression model that links PCI with IRI. Measured pavement distresses from 1448 LTPP sections from the Specific Pavement Studies (SPS) and General Pavement Studies (GPS) representing 12744 data points were utilised for the PCI estimation. A total of 1208 sections with 10868 data points were used for model development while 240 sections with 1876 data points were used for the model validation. A sigmoid function is found to best express the relationship between PCI and IRI with a coefficient of determination (R2) of 0.995. The bias in the predicted IRI values is significantly very low. The model validation using a different dataset also yielded highly accurate predictions (R2 = 0.992). Finally, a pavement condition rating based on IRI is proposed. This system yields rating equivalent to the widely used PCI rating method which is based on the pavement condition. [ABSTRACT FROM AUTHOR]

Structural Number (SN) is a numerical value used as pavement structural capacity indicator. This paper reviews the most recognised historical SN prediction models. These models are COST, Schnoor and Horak, Kavussi et al., Rohde, and Kim et al. These models predict the structural number of existing flexible pavement systems (SNeff) based primarily on the Falling Weight Deflectometer (FWD) data. One major drawback of these models, is that they ignore the effect of temperature on the backcalculated modulus of the Asphalt Concrete (AC) layer and hence the predicted SNeff values. The accuracy of the investigated SNeff prediction models after applying temperature correction to the AC layer modulus (EAC) and the FWD peak deflection (Do) to a reference temperature of 21°C was examined. FWD data and backcalculated moduli of pavement layers were collected from the Long Term Pavement Performance (LTPP) database. Fourteen pavement test sections covering the four climatic regions in the U.S. with 1293 FWD test points were used to evaluate and improve the accuracy of the investigated models compared to the AASHTO 1993 method. The most prominent models were calibrated and/or simplified. The proposed calibrated/simplified models produced more accurate and less biased SNeff predictions as compared to the original models. The proposed modified models were validated using another set of LTPP data and they yielded comparable predictions. [ABSTRACT FROM AUTHOR]

International Roughness Index (IRI) is a pavement performance indicator which reflects not only the pavement condition but also the ride quality and comfort level of road users. The aim of this paper is to develop an accurate IRI prediction model for flexible pavements using both multiple linear regression analysis and artificial neural networks (ANNs). The models were developed based on the Long-Term Pavement Performance Database. The data were collected for both original and overlaid flexible pavements from the general pavement studies (GPS-1, GPS-2 and GPS-6) and the specific pavement studies (SPS-1, SPS-3 and SPS-5). The final database consisted of 506 sections with 2439 observations. The proposed models (regression and ANNs) predict IRI as a function of pavement age, initial IRI (IRI just after pavement construction), transverse cracks, alligator cracks and standard deviation of the rut depth. The regression model yielded a coefficient of determination (R2) value of 0.57 while the ANNs model resulted in a much higher R2 value of 0.75. [ABSTRACT FROM AUTHOR]