Jointed Plain Concrete Pavement Research Articles

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

Concrete pavement buckling is a significant distress that disrupts traffic flow and results in costly repairs, issues that are increasingly exacerbated by global warming. Addressing these issues, this study develops a systematic framework for assessing jointed plain concrete pavement (JPCP) by integrating temperature and moisture gradients, pavement structures, and material properties into mechanical-based models for predicting buckling potential. Specifically, a two-stage analysis is developed to analyze buckling development based on the slab deformation and the slab-base interface, including joint closure assessment and upheaval buckling prediction. Through the established model, parametric studies are conducted to quantitatively identify key factors influencing buckling. Moreover, the developed models are applied to pavement sections in various climate regions and validated through comparative analysis with field data from the Long-Term Pavement Performance (LTPP) database to confirm model accuracy. Additionally, a case study using pavement buckling data from Wisconsin between 2014 and 2020 demonstrates the model's practical application. As the results, it is found that temperature increase, the percentage of incompressible materials, and the coefficient of thermal expansion are critical for joint opening changes, while the coefficient of thermal expansion, elastic modulus, and slab thickness are crucial to post-buckling performance. Regression equations are provided to estimate safe temperature increases as a recommended buckling temperature limit. Furthermore, the predicted joint opening shows reasonable alignment with joint gauge measurement in the field recordings from the LTPP database. Finally, the predicted buckling events are reasonably compared with field recordings for a Wisconsin pavement section from 2014 to 2020. In summary, by considering pavement structural parameters, material properties, and weather conditions, this comprehensive model provides a reasonable tool for predicting and mitigating JPCP buckling in different climate conditions.

A Parametric Study Investigating the Dowel Bar Load Transfer Efficiency in Jointed Plain Concrete Pavement Using a Finite Element Model

Transverse joints are introduced in jointed plain concrete pavement systems to mitigate the risk of cracks that can develop due to shrinkage and temperature variations. However, the structural behaviour of jointed plain concrete pavement (JPCP) is significantly affected by the transverse joint, as it creates a discontinuity between adjacent slabs. The performance of JPCP at the transverse joints is enhanced by providing steel dowel bars in the traffic direction. The dowel bar provides reliable transfer of traffic loads from the loaded side of the joint to the unloaded side, known as load transfer efficiency (LTE) or joint efficiency (JE). Furthermore, dowel bars contribute to the slab’s alignment in the JPCP. Joints are the critical component of concrete pavements that can lead to various distresses, necessitating rehabilitation. The Swedish Transport Administration (Trafikverket) is concerned with the repair of concrete pavement. Precast concrete slabs are efficient for repairing concrete pavement, but their performance relies on well-functioning dowel bars. In this study, a three-dimensional finite element model (3D-FEM) was developed using the ABAQUS software to evaluate the structural response of JPCP and analyse the flexural stress concentration in the concrete slab by considering the dowel bar at three different locations (i.e., at the concrete slabs’ top, bottom, and mid-height). Furthermore, the structural response of JPCP was also investigated for several important parameters, such as the joint opening between adjacent slabs, mispositioning of dowel bars (horizontal, vertical, and longitudinal translations), size (diameter) of the dowel bar, and bond between the slab and the dowel bar. The study found that the maximum LTE occurred when the dowel bar was positioned at the mid-depth of the concrete slab. An increase in the dowel bar diameter yielded a 3% increase in LTE. Conversely, the increase in the joint opening between slabs led to a 2.1% decrease in LTE. Additionally, the mispositioning of dowel bars in the horizontal and longitudinal directions showed a 2.1% difference in the LTE. However, a 0.5% reduction in the LTE was observed for a vertical translation. Moreover, an approximately 0.5% increase in LTE was observed when there was improved bonding between the concrete slab and dowel bar. These findings can be valuable in designing and evaluating dowel-jointed plain concrete pavements.

Concrete Pavement Design Analysis Using AASHTOWare Pavement Mechanistic-Empirical Design Software

Local calibration of pavement performance models embedded in the AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software may be needed to implement this design methodology. A recent survey showed that, despite local calibration, several state highway agencies in the U.S. are using the American Association of State Highway Transportation Officials (AASHTO) Guide for Design of Pavement Structures (1993 version) and the PMED software for comparative design of jointed plain concrete pavement (JPCP). This paper describes the recalibration process of the JPCP distress models in the PMED software and comparative designs obtained by the PMED software and 1993 AASHTO design guide. The results show that the calibration/recalibration process depends on the availability of measured performance data. Not all global coefficients must be changed in the calibration/recalibration process. The 1993 AASHTO design guide yielded higher JPCP slab thickness for projects with high truck traffic than the PMED software when no friction is assumed between the slab and a cementitious base. An assumption of full friction will have a discernible effect on the slab thickness obtained from PMED.

The analysis and design of jointed plain concrete pavements with wider slabs

Despite the cost-effective nature of asphalt pavements, concrete pavements are preferred for their superior serviceability. Many agencies use Jointed Plain Concrete Pavement (JPCP) designs with a standard slab size of 3.5 × 4.5 m, focusing on transverse cracking. However, night-time Longitudinal Top-Down Cracking (LTDC) can occur in widened slabs due to tandem and tridem axles. This study employs a Finite Element (FE) approach to assess wider slabs under various loads. Longitudinal tensile stresses at critical points were found to increase with the slab width. Therefore, the study emphasizes the need for a design approach addressing transverse and longitudinal cracking. The design of the wider slabs (4.5 × 4.5 and 5.0 × 4.5 m) is demonstrated using a case study. If the longitudinal joint in a 9.0 m slab is placed at 5.0 m from the edge, the pavement could perform better than other slab sizes. However, it is necessary to design the pavement considering both longitudinal and transverse cracking.

Quantifying the Effects of Material Input Levels on Jointed Plain Concrete Pavement (JPCP) Performance and Slab Thickness

The mechanistic-empirical pavement design guide (MEPDG) is a commonly accepted design principles guide that aids in jointed plain concrete pavement (JPCP) design and performance analysis. The MEPDG uses three different design parameter input levels, referred to as level one, level two, and level three, providing increasing confidence in the analysis at the lower numbered levels, which use more locally relevant (level two) or project-specific (level one) data. The state-of-the-art pavement ME software (version 2.6.2) uses MEPDG design principles to predict pavement performance. The three performance indicators for JPCP systems (international roughness index (IRI), joint faulting, and transverse cracking) experience significant changes when simulating under a different input level. The IRI and faulting indicator changed by 78 percent when using inputs varying from level one to level three, with the cracking indicator change being more severe at 87 percent. To accommodate the change in performance indicator values between input level one and input level three, increasing the concrete slab thickness is necessary to achieve comparable pavement performance. An increase in the Portland cement concrete (PCC) layer from one inch to two inches is required when input level three simulations are performed, demonstrating the economic and sustainability benefits of using project-specific level one inputs. Understanding the impact of simulation input levels will help to meet design and sustainability goals and improve the lifecycle performance of JPCP systems.

Análise da adequação da reciclagem do pavimento de concreto do Rodoanel Metropolitano de São Paulo como novo pavimento

Abstract The west section of the Governor Mário Covas São Paulo Metropolitan Road Ring was opened to traffic in 2002. The original pavement was a jointed plain concrete pavement with dowelled joints and a design flexural strength (or modulus of rupture) of 4.5 MPa. Premature failure of the concrete slabs was observed not only by engineers and users, but also reported by the media. Concrete slabs soon started being demolished and replaced. Many of the removed slabs were taken to a nearby recycling plant for crushing. However, the resulting recycled concrete aggregate was not used in the manufacturing of the new slabs. There were concerns that the potential presence of detrimental chemical substances had caused the early deterioration of the slabs. If that were true, using recycled aggregates obtained from the original concrete could result in the same issues in the new mix. This paper describes a feasibility study on recycling the original concrete for use as aggregate in new concrete slabs. The first goal was to verify the presence of fluorite as detrimental substance in the existing concrete because there was a suspicion among concrete technologists engaged on mixture design during the construction. Chemical analysis using X-ray fluorescence spectrometry did not show the presence of chemical components detrimental to concrete durability. Therefore, the second goal was to accept the material for recycling and discuss mix proportions, strength, stiffness, fracture toughness, and fatigue. Although some of the concrete properties were affected by the inclusion of recycled aggregates in the mix, the results indicate that it is possible to use recycled aggregates produced from the original concrete to cast new concrete slabs, however requiring greater thicknesses. The fatigue performance of recycled mixtures points out the demand for thicker and eventually more expansive design solutions.

Fatigue Damage Prediction for Superload Vehicles in Pennsylvania on Jointed Plain Concrete Pavements

Fatigue Damage Prediction for Superload Vehicles in Pennsylvania on Jointed Plain Concrete Pavements

Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model

Faulting refers to the common and significant distress in Jointed Plain Concrete Pavement (JPCP), which has an adverse impact on the pavement roughness. Nevertheless, the existing fault prediction models continue to heavily rely on conventional linear regression techniques or basic machine learning approaches, which leaves room for improvement in training efficiency and interpretability. To enhance training efficiency and accuracy, this study developed five novel faulting prediction models. These models are based on five basic machine learning algorithms: Random Forest (RF), Additive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), and Categorical Boost (CatBoost), combined with the tree-structured Parzen estimator (TPE). The five models are TPE-RF, TPE-AdaBoost, TPE-GBDT, TPE-LightGBM, and TPE-CatBoost. In addition to selecting the best-performing model, this study incorporated the Shapley Additive Explanation (SHAP) technique and developed TPE-SHAP-CatBoost to improve the interpretability of the model’s predictions. The process involved extracting historical data on pavement performance, including 17 variables, from the Long-Term Pavement Performance (LTPP) database for 160 instances of observation. Firstly, the Boruta method was used to identify the final set of input variables. Secondly, the TPE technique, which is a Bayesian optimization method, was applied to automatically select the optimal hyperparameters for the base models. Finally, SHAP was used to provide both global and local explanations of the model’s outputs. The results indicate that the TPE-CatBoost model achieves the highest accuracy with an R2 value of 0.906. Furthermore, the TPE-SHAP-CatBoost model identified the primary factors influencing faulting by incorporating SHAP and provided explanations of the model’s results at both the global and local levels. These research findings highlight the ability of the proposed model to accurately predict faulting, providing precise and interpretable guidance for pavement maintenance while reducing workload for pavement engineers in data collection and management.

An artificial-intelligence based approach for predicting structural damages of paved-road systems under superloads

When studies on mechanistic-based pavement analysis and design are actively conducted, highly-accurate and meaningful data related to structural damages caused by traffic loads have accumulated. Although widely-used pavement design programs such as the American Association of State Highway and Transportation Officials (AASHTO)Ware Pavement Mechanistic-Empirical (M-E) Design software supports M-E pavement design for comprehensive pavement structures and traffic loadings, it is still unable to predict extrapolated mechanistic responses when pavements are subjected to superloads having non-standardized loading configurations not yet included in the software. In this study, artificial neural-network (ANN)-based surrogate models were developed and optimized to provide high accuracy in predicting critical pavement responses related to representative structural damages of jointed plain-concrete pavements (JPCPs) and flexible pavements when subjected to a single pass of various superload types, thereby extensively broadening the scope of constrained mappings in terms of loading variables. Sensitivity analysis on pavement structural and loading variables was performed using the ANN models developed in this study to identify the significance level of each explanatory variable in generating target-pavement responses.

Dynamic Response Analysis of JPCP with Different Roughness Levels under Moving Axle Load Using a Numerical Methodology

In-service Portland cement concrete (PCC) pavements are subject to repeated dynamic loads from moving vehicles; thus, the actual stress generated in a PCC pavement may significantly differ from the static stress, which is normally used in the design and evaluation of pavement performance. Calculating the stress in PCC pavements under moving vehicle loads is of importance to assess their actual service condition, particularly for pavements with different surface roughness levels as the deteriorated roughness might cause large stress in PCC pavement subject to dynamic loads. In this paper, a method is proposed to compute the dynamic response in terms of loads and stresses generated in jointed plain concrete pavements (JPCPs) under a moving axle load, considering the effects of the pavement surface roughness, the vehicle parameters (including vehicle speeds and axle weights), and the pavement structure parameters (including thickness and elastic modulus of different layers and the existence of dowel bars). The dynamic axle load is firstly generated based on the quarter-car model, running through three successive slabs of which the surface roughness is determined by the power spectral density method, and the critical locations in slabs where the largest tensile stresses occur are identified. The combined effects of various pavement surface roughness levels, vehicle speeds, axle weights, and pavement structure parameters are evaluated in terms of the stress and the dynamic factor defined as the ratio of the tensile stress under dynamic load to the tensile stress under static load. For the roughness level D, the tensile stress can reach a maximum value of 3.13 MPa, and the dynamic factor can reach a maximum value of 2.46, which is much larger than the dynamic factor of 1.15 or 1.2 currently used in design guidebooks. Increasing the thicknesses of pavement slab or the subbase layer is an effective way to reduce the tensile stress in JPCP, while increasing the thickness of base layer is not effective. The results of this study can benefit future pavement design and pavement performance evaluation by providing the actual stress and the useful dynamic factor values for various conditions of field pavements. Moreover, preventive maintenance, particularly the improvement of pavement surface roughness, can be planned by referring to the results of this study, to avoid large tensile stress generated in JPCPs.

Comparative Analysis of Jointed Plain Concrete Pavement and Roller-Compacted Concrete Pavement

Jointed plain concrete pavement (JPCP) is the widely preferred conventional concrete pavement for highways; however, the higher initial construction cost and extended time for traffic opening limit its applicability. Recent advancements like roller-compacted concrete pavement (RCCP) could address the above concerns of JPCP. Yet, there is a lack of knowledge and comparative studies on the performance assessment, sustainability, and economic analysis of RCCP and JPCP. Therefore, this study aims to evaluate the engineering properties (density, compressive and flexural strength, and fluid flow properties), cost analysis (materials and initial construction cost), and sustainability (carbon dioxide emissions and energy consumption) of both pavements. The fresh density and mechanical properties of RCCP were 8%–9% and 13%–35% higher than JPCP; thus, 44%–89% and 24%–57% reductions in water absorption and porosity were observed in the considered RCCP mixes against JPCP. The higher strength properties of RCCP significantly reduced the pavement thickness by 10%–25%, thereby lowering the materials and initial construction costs by 52%–78%. Further, sustainability assessment through the cradle-to-gate (from raw material extraction to transportation phase) and gate-to-gate (only concrete production in a ready-mix plant) approaches revealed that employment of RCCP could significantly reduce carbon dioxide emissions (9%–30%) and energy consumption (10%–32%) compared with JPCP mixes. The findings show that RCCP can be considered sustainable, economical, and higher-performance pavement than JPCP.

Asset Valuation Model for Highway Rigid Pavements Applicable in Public–Private Partnerships Projects

Recently, in Chile, infrastructure asset value has been incorporated into highway concession contracts. However, the current valuation model used for rigid pavements is not adapted to the standards and conditions of such projects. This study develops a valuation model for rigid pavements of interurban highway concessions and evaluates it in a case study. The proposed model captures the loss in asset value associated with the performance degradation over time, considering a typical Jointed Plain Concrete Pavement (JCPC) configuration. The value is calculated using performance indicators that represent the structural capacity and level of service provided to road users. The model represents a significant improvement compared to current asset valuation models used in highway concessions. It enables the public agency to objectively evaluate the preservation of asset value carried out by the private partner during the concession. Additionally, it could also be used as a tool to establish payments between infrastructure stakeholders. Some of the concepts applied could also be relevant for other highway assets existing in Public–Private Partnership (PPP) projects.

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

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).

Approach to Estimate Time to Friction Loss in Pavement Mechanistic-Empirical for the Florida Concrete Test Road

Local calibration of the fatigue cracking model used in the AASHTO Pavement Mechanistic-Empirical (ME) software is critical to accurately prediction of the performance of jointed plain concrete pavement (JPCP). Although calibration involves optimizing coefficients used in the model, other design inputs such as time to friction loss can have a significant impact on pavement performance. The degradation of friction between the concrete and supporting layers is a critical input since it indicates deterioration of the JPCP system before fatigue cracking is observed on the surface. This paper proposes an approach for estimating the time to friction loss for JPCP based on the relationship between effective thickness and pavement age determined with falling weight deflectometer (FWD) data. This study shows that using the estimated time to full friction loss rather than the default time causes a significant impact on pavement performance compared with only optimizing calibration coefficients. Ten sections with a similar climatic zone and JPCP structures to Florida’s concrete test road were considered in the development of a relationship between the time to full friction loss and pavement structure thickness. The relationship was used to estimate the expected time to friction loss for the Florida concrete test road as approximately 55 months for sections with a concrete thickness of 7 in. and 164 months for concrete sections of 10 in.

Evaluating the impact of overweight trucks on rigid pavement performance using AASHTOWare pavement ME design

ABSTRACT The purpose of this study is to evaluate the impact of overweight vehicles on pavement performance over its design life, with Jointed Plain Concrete Pavements (JPCPs) particularly considered for analysis. The Long-Term Pavement Performance database was used to provide information on pavement construction, age, overall structure, specific traffic inputs, and recorded historical pavement performance. AASHTOWare Pavement ME Design (PMED) was used to analyze data extracted from LTPP based on legislation by the Federal Highway Administration (FHWA) regulating gross vehicle weight (GVW) and single and tandem axle weights of overweight trucks (OWTs) to estimate pavement performance and service life. The overall findings indicated that although increasing overweight by 20% on single or tandem axles could considerably reduce pavement service life by up to 20% in the various scenarios examined in this study, OWT exceeding the GVW limit legally-allowed by FHWA could damage pavement more than OWT exceeding axle weight limits.

Reduction of critical positive temperature gradients in jointed plain concrete pavements

ABSTRACT Temperature gradients in concrete pavements significantly affect fatigue damage accumulation, with high gradients having the potential to reduce performance under vehicle loads. While temperature profiles are non-linear throughout the depth, an equivalent linear temperature gradient (ELTG) accounts for the effect of temperature in pavement design. Critical ELTG values are difficult to predict because ELTG is affected by design parameters as well as climate conditions and varies diurnally and seasonally. To identify critical parameters which affect ELTG, a database of hourly temperature profiles was generated for a series of jointed plain concrete pavements (JPCP) throughout Pennsylvania. A stepwise regression model was developed with an R2 of 0.81 to determine maximum daily ELTG as a function of the design parameters and climate. This equation can assist engineers in making decisions on when superloads can be transported without significant damage or performing falling weight deflectometer testing will be most informative. Slab thickness, concrete albedo, and daily temperature range (DTR) were the most significant parameters that affected ELTG. A higher albedo showed an equivalent effect in reducing maximum ELTG as increasing the thickness of the slab. This finding can be used to reduce maximum gradients, which will increase the predicted service life of JPCPs.

Temporal and Spatial Variation in Drying Shrinkage and Thermal Response of a Jointed Plain Concrete Pavement

Drying shrinkage and coefficient of thermal expansion (CTE) are key material properties that affect the long-term performance of a concrete pavement. The importance of these two properties will continue to increase as extreme heat events occur more frequently with climate change. Standard laboratory tests performed to quantify these properties do not replicate the in-situ slab conditions such as spatial and temporal variation in moisture levels. As a result, laboratory-determined material property values may not accurately reflect the response of the pavement to changes in temperature. In this research, strain data from a 16-year-old in-service jointed plain concrete pavement was studied with a focus on the variation in drying shrinkage and response to temperature change. Drying shrinkage over time was estimated at different locations within the slab. Additionally, the structural coefficient of thermal expansion (SCTE) was determined as the strain response of the slab to unit changes in temperature. The frequency and amount of monthly rainfall was compared with SCTE results to explain seasonal variation in SCTE values. The results indicate that slab restraint conditions affect both the development of drying shrinkage and the SCTE of the pavement. Additionally, this study shows that both drying shrinkage and SCTE are not constant through the pavement structure, and both parameters are affected by the moisture content of the concrete. The SCTE was up to 23% larger and 22% smaller than the CTE determined through laboratory testing. In addition, only 31% of the laboratory measured ultimate drying shrinkage was realized in the slab.

Development of Pavement Performance and Remaining Service Life Prediction Tools for Iowa Jointed Plain Concrete Pavement Systems

Moving Ahead for Progress in the Twenty-First Century (MAP-21) requires US state highway agencies (SHA) to utilize performance-based approaches in their pavement management decision-making processes, and use of a remaining service life (RSL) model would be one of such performance-based approaches for facilitating the pavement management decision-making process for SHAs. In this study, statistical and artificial neural network (ANN)-based pavement performance and RSL models were developed for Iowa jointed plain concrete pavement systems (JPCP) using actual pavement structural, traffic, construction history, and pavement performance records obtained from the Iowa Department of Transportation pavement-management information system database. While both models were found to be potentially useful for project and network level performance and RSL predictions, statistical and ANN-based models were respectively found to be more suitable for project and network level analysis. Using these models, efficient Microsoft Excel-based automation tools were created to predict future performance of a JPCP section and estimate RSL values based on predicted future performance and threshold limits for the performance indicators. Consequence analysis was also conducted to investigate the impact of traffic and preservation treatment (diamond grinding) on the RSL of a JPCP. The tool, also capable of estimating realistic pavement pretreatment and posttreatment performance and RSL, could be successfully used as part of performance-based pavement management strategies and helping decision-makers make better-informed pavement management decisions to properly allocate agency resource expenditures. Moreover, this study provides a better understanding of RSL and the factors that influence both the project and network level RSL.

Evaluation of the effect of metallurgical aggregates (steel and copper slag) on the thermal conductivity and mechanical properties of concrete in jointed plain concrete pavements (JPCP)

Thermal stresses are created in concrete pavements during the day and night due to the rise and fall in temperature. These stresses are very important in concrete since they lead to cracking and a reduction in the bearing capacity. The cause of these stresses is the low thermal conductivity of concrete, which stands in contrast to the materials which release the induced thermal stresses easily thanks to their high thermal conductivity. Since aggregates make up most of the volume of concrete, any change in aggregates proportion will have a significant impact on concrete properties. In this research, the effect of using copper slag and steel slag instead of natural aggregate on the thermal conductivity and mechanical properties of concrete has been evaluated. The reasons for using these two types of slag in concrete are helping with the disposal of these industrial wastes from the environment and preserving the resources of stone mines. Another reason is the suitable physical and chemical properties these two materials offer when used in concrete. Three types of natural aggregates, copper slag, and steel slag were used in 5 different mixing designs to make the test samples. In the prepared concrete samples, the ratio of aggregate volume to the total mixture, the amount of water, and the cement amount were fixed. The variable of this study was the percentage of aggregates used in concrete mixtures, which were zero, 50 and 100 % of the mixture volume. In this research, a range of tests, including slump tests, density of fresh concrete, ultrasonic pulse velocity, compressive strength, indirect tensile strength, flexural, abrasion resistance, and thermal conductivity test were performed. According to the obtained results, the concrete samples made of copper slag aggregate (100CSA) saw an increase of 20 MPa in compressive strength after 28 days of processing compared to samples with natural aggregate. Copper slag aggregate samples (100CSA) also experienced an increase of 2 MPa, 2.8 MPa and 10 mm in their indirect tensile strength, flexural, and slump resistance of concrete compared to samples with natural aggregate. In the test of abrasion resistance and thermal conductivity, samples with natural aggregate had best performance. Steel slag aggregate and copper slag aggregate reduced thermal conductivity of concrete by 23 % and 4.5 % respectively compared to the natural aggregate sample. Finally, according to the experiment results and the use of Vikor statistical method, it was revealed that concrete samples made of 50 % copper slag aggregate and 50 % natural aggregate (50NA + 50CSA) have the best performance among all other designs.

Optimized prediction models for faulting failure of Jointed Plain concrete pavement using the metaheuristic optimization algorithms

This study aims to predict faulting failure of jointed plain concrete pavement (JPCP) using different variables. For this purpose, four feature selection methods were developed by combining the artificial neural networks (ANN) and four multi-objective metaheuristic optimization algorithms, namely, the Pareto envelope-based selection algorithm II (PESA-2), the strength Pareto evolutionary algorithm 2 (SPEA-2), multi-objective particle swarm optimization (MPSO), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D showed better performance compared to the other models, which identified 17 input variables affecting faulting failure. In the next step, the classic back-propagation (BP), Biogeography-based optimization (BBO), invasive weed optimization (IWO), and simulated annealing algorithm (SAA) were combined with the ANN to develop three prediction models for faulting failure. Modeling with metaheuristic optimization algorithms showed better performance than the ordinary ANN. The pavement age, cumulative average precipitation, and elasticity modulus of the concrete slab have the most significant impact on the formation and increase of faulting.