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The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model’s effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model.

Time and cost are essential criteria for analyzing project feasibility. Project managers analyze the cost and duration of projects and make trade-offs between them before project initiation. During the implementation phase of a given project, a delay exists, making the initial plan impractical. Additionally, the contractor must pay a certain amount of money as delay fine based on the contract or spends extra money in order to reduce the duration of the project. This study proposes a new method to consider a trade-off between these two alternatives as a way to minimize the total time and the total extra money that should be paid. To this end, four strategies–minimizing costs, omitting delay under a minimum budget, minimizing cost and delay of the project simultaneously, and reducing the delay up to a particular level under a minimum budget–are taken into account to help decision-makers make the best decision. A case study is presented in this work, and 13 swarm intelligence and evolutionary algorithms are applied to find optimal solutions. A new index is developed and is used to compare various strategies and different algorithms. Based on the results, the introduced approach can reduce project costs and project delays by 28.8% and 85.7%, respectively. Moreover, the cuckoo search algorithm, invasive weed optimization, coyote optimization algorithm, and differential evolutionary algorithm outperform the other algorithms based on outcomes and the Tukey pairwise comparison results. Furthermore, the firefly algorithm is recognized as being the fastest algorithm for solving a delay time-cost trade-off problem.

Maintenance and Rehabilitation (M&R) scheduling is one of the vital aspects of a pavement management system (PMS). This study aims to establish accurate M&R plans for a large-scale pavement network. To this intent, parameters affecting pavement deterioration were identified from the literature, then Random Forest Regression was employed to determine the effective features for pavement deterioration modelling. An accurate pavement deterioration function was generated by applying significant features. The most robust metaheuristic and evolutionary algorithms were selected and adjusted to solve the M&R scheduling optimisation problem, including the Water Cycle Algorithm (WCA), Arithmetic Optimisation Algorithm (AOA), Differential Evolutionary (DE), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Genetic Algorithm (GA). The performance of the mentioned algorithms was compared to help researchers and decision-makers to select the appropriate algorithm for M&R scheduling optimisation. WCA and AOA showed to have the best performance among the adapted algorithms. Compared to AOA, DE, ACO, PSO, and GA, WCA's objective function was calculated to be 45%, 74%, 74%, 77% and 83% less, while its M&R cost was cheaper by 13%, 16%, 27%, 19%, and 18%, respectively.

CO2 emission reduction in large-scale pavement network maintenance planning has been an immense concern. The conventional single-objective optimisation modelsoverlook environmental issues such as C...

Nowadays, management of construction holdings has become more complex. Studies indicate less than one-fifth of holdings are successful in implementing strategies and gaining from evaluation processes. Selecting a suitable method for measuring the performance will enhance management Control capabilities. On the other hand, the weakness of traditional measurement systems and the changing competitive environment has necessitated the use of new performance measurement method. Different frameworks were used for evaluating construction holdings neglecting scientific procedure considering these organizations’ structure. The challenge would be defining a model to tailor a performance-measuring framework for each construction holding. The research method is based on content analysis and statistical computation. The knowledge base provided in this article makes tailoring the best-fitted performance measurement model for each holding possible, considering their characteristics. Therefore, construction holdings management will be based on a result-oriented measurement using performance measurement. Studying the characteristics of construction holdings and investigating on application of each performance measurement framework would be the data gathering part of research. Then, using content analyses and appropriate decision-making techniques, the knowledge base of each framework capability in any different characteristic categories is achieved. Having this tool, the performance-based management solution for a specific case will be studied. Objective of this research is to develop a validated performance measurement model tailorable for each construction holding; enhancing the performance, by the best possible measurement model.

This study approaches a multi-stage stochastic mixed-integer programming model for the high-level complexity of large-scale pavement maintenance scheduling problems. The substance of some parameters in the mentioned problems is uncertain. Ignoring the uncertainty of these parameters in the pavement maintenance scheduling problems may lead to suboptimal solutions and unstable pavement conditions. In this study, annual budget and pavement deterioration rate are considered uncertain parameters. On the other hand, pavement agencies generally face large-scale pavement networks. The complexity of the proposed stochastic model increases exponentially with the number of network sections and scenarios. The problem is solved using the Progressive Hedging Algorithm (PHA), which is suitable for large-scale stochastic programming problems, by achieving an effective decomposition over scenarios. A modified adaptive strategy for choosing the penalty parameter value is applied that aims to improve the solution process. A pavement network including 251 sections is considered the case study for this investigation, and the current study seeks optimal maintenance scheduling over a finite analysis period. The performance of the stochastic model is compared with that of the deterministic model. The results indicate that the introduced approach is competent to address uncertainty in maintenance and rehabilitation problems.

The majority of sensors used for quantifying indices related to a pavement management system are costly; however, low-cost sensors such as RGB-D sensors have been recently deployed. The performance of RGB-D sensors in pavement condition data collection has not received enough attention. By providing a thorough literature review on the specification of RGB-D sensors, this paper aims to assess and compare the performance of two RGB-D sensors in pavement condition data collection. First, by collecting data from asphalt, concrete, and mosaic surfaces in wet and dry conditions, the accuracy and precision of the sensors are evaluated. For this purpose, noise reduction techniques are applied to the central region of interest and the inherent trend of depth frames is corrected by implementing the Singular Value Decomposition algorithm. Second, dimensions of artificial objects above the pavement surface are detected via sensors by using the Lease Square Method (LSM) and edge detection techniques to evaluate RGB-D sensors' ability and performance in measurement of artificial pavement defects of known dimensions. The results of statistical analysis show the difference in error measurement via both sensors in all surfaces in wet and dry conditions. Through conducting extensive field experiments, it is concluded that Kinect one has several advantages over the Kinect Xbox 360 in pavement condition data collection.

Data acquisition and data processing are at the core of pavement management systems. Nowadays, traditional methods of data collection are rarely used in developed countries due to the considerable disadvantages of the traditional methods compared to the automated ones, such as the slow pace of data collection, endangering the safety of the human operators collecting the data, considerable cost, collecting data from only a limited section of road networks, and inconsistency among the data collected by different operators. In contrast, automated methods alleviate the majority of these problems. However, the main drawback of the automated methods is the high cost of purchase, implementation, and maintenance of the equipment, which has deterred their use in developing countries with limited financial resources. To address this problem, developing a new automated method that reduces the production costs while keeping the required accuracy and performance seems imperative. The goal of this research is to investigate the use of microphones, as inexpensive equipment with acceptable accuracy, for collecting pavement macrotexture data, which is an input to the pavement management system. The proposed method is based on the tire/road interaction noise. To this end, a review of the previous researches on audio-based monitoring of various pavement features is presented. By considering the results of the related researches and the goals of this work, a new setup for data collection and an accompanying signal processing method is proposed. To develop and evaluate the proposed method, data from six standard road sections of a test field are collected. To process the collected data, PCA, Cepstrum, LPC, LSF, PSD, and Wavelet methods are employed. The SVM and KNN classification methods are used to evaluate the results of the signal processing step, which is performed in various frequency bands. The best results are obtained by using the Cepstrum signal processing method along with the SVM classifier in the 3000–5000 Hz frequency band resulting in an accuracy of 95% on the test data and the precision error of 1%.

Appropriate pavement maintenance is of great importance due to the increasing deterioration of pavements and limited resources. Nowadays, highway agencies face large-scale networks. The management ...

Pavement roughness is one of the essential parameters of pavement quality affecting ride quality, driving safety, and fuel consumption. The International Roughness Index has been widely deployed to...

Nowadays, probabilistic prediction models commonly represented by Markov Chain Process (MCP) attract more attention in pavement management systems. Additionally, roughness condition indices provide...

© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. One of the key parts of a pavement management system is the maintenance and rehabilitation planning. The planning is usually developed under the assumption that all parameters are known with certainty. In practice, there are various parameters afflicted with large uncertainty. Ignoring the uncertainty may lead to a suboptimal plan adversely affecting the network conditions. The objective of this study is to develop an optimisation framework for network-level pavement maintenance and rehabilitation planning considering the uncertain nature of pavement deterioration and the budget with an applicable approach. A multistage stochastic mixed-integer programming model is proposed to find the optimal plan that is feasible for all possible scenarios of uncertainty and optimise the expectation of objective function. Two case studies of 4 and 21 pavement sections are presented to show the applicability of the proposed method. The value of stochastic solution and the expected value of perfect information which are the indices for evaluating the benefits of using the stochastic model are, respectively, 30% and 85% of the objective function of here and now model for the first case study and 26% and 42% of it regarding the second one. The indices are high indicating the effectiveness of the stochastic solution.

Data collection of the pavement condition is the main core of the pavement management systems. The automated data collection method has some advantages over the traditional one such as safety, consistency, speed, and repeatability. However, one of the main disadvantages of the automated data collection is the high cost of required devices. It is necessary to present an innovative cost-effective automated data collection system with adequate accuracy and precision. Pavement roughness is one of the most important parameters showing the pavement condition and directly affecting the ride quality. The main aim of this research is to develop a new automated cost-effective data collection device for measuring roughness. An extra wheel equipped with accelerometers and a geographical positioning system are utilized for evaluating pavement roughness. Longitudinal profiles are employed to calculate the International Roughness Index through the application of a linear model with a high coefficient of determination of 0.87. The root-mean-square error and the average error percentage are sufficiently low, equal 10% and 20%, respectively.

Determination and measurement of the characteristics of surface distresses such as cracking are the important tasks of pavement maintenance authorities. The measurement of main characteristics incl...

Moisture damage is a major concern for evaluating the performance of asphalt mixtures. There are different types of experimental methods to determine the effect of moisture on mechanical an...

An airport pavement management system is of great significance in terms of allocating budget and resources as well as preserving the level of serviceability and safety. In order to provide a more a...

Pervious concrete is one of the most effective environmentally friendly products which can be used as a paving material to diminish runoff and increase safety. To better understand the behaviour of...

Road surface friction is one of the most important parameters determining road safety and macrotexture is one of the important factors affecting it. There are two types of traditional and automated data gathering methods. One of the disadvantages of automated methods is the high cost of purchasing or manufacturing. The purpose of this study is evaluating the feasibility of a low-cost dynamic method based on tire/road noise to evaluate the macrotexture of cracked non-porous asphalt pavements. To this end, the tire/road noise gathering system has been developed inspired by the ISO 11819-2 standard. The developed system has been used for data collection alongside with the health monitoring system of the Amirkabir University of Technology. After data gathering, the relationship between macrotexture indices (laser-based) and tire/road noise features has been evaluated. Using artificial neural networks, models for relating different sound and macrotexture indices in each frequency band are presented with the best correlation result being R2 = 0.76 between MPD and power spectral density in the 500 to 1500 Hz frequency band. The results indicate that in cracked pavement surfaces, by focusing on the vibration mechanism, acceptable results can be achieved in macrotexture monitoring by using interaction noise.

The application of waste wood-based bio-oil in asphalt binder has illustrated many performance advantages for the pavement. However, the introduction of bio-oil to asphalt binder has challenges associated with poor high-temperature performance. To address this matter, the liquefied 4,4′-diphenylmethane diisocyanate (MDI) was utilized as an additive to enhance the high-temperature performance of wood-based bio-oil asphalt (WBBA), while not reducing its low-temperature properties in this paper. Five ratios of MDI (0%, 0.5%, 1%, 2%, and 4%, by weight of bio-oil asphalt) were selected to prepare MDI modified wood-based bio-oil asphalt binder (MDI-WBBA). Fourier Transform Infrared Spectroscopy and Rod Thin Layer Chromatograph were conducted to explore the modification mechanism, while temperature sweep, frequency sweep and bending beam rheometer test were carried out to evaluate the rheological performance of MDI-WBBA. This study aims to correlate the observed rheological behaviors with the chemical reactions occurring during the process of MDI modification. The results indicated that the MDI modification was a chemical process, and the polyurethane and urea linkage in reaction polymers could easily lead to hydrogen bonds, which could increase the physical cross-linking density, and further enhance the strength and elasticity of the MDI-WBBA. Meanwhile, the asphaltenes significantly increased and the modified asphalt binder gradually tended to gel type. In particular, the colloidal structure remained stable when the amount of MDI reaches 2%. Furthermore, the high temperature and viscoelastic properties significantly enhanced through the black curve and master curve of G*, while the low-temperature behavior experienced a slight rise with the addition of MDI, which could provide an efficient method to alleviate the environmental problems of waste mass.

This study proposes an inexpensive system using a linear laser and a high speed-shot sports camera to compute the rutting depth in static and dynamic modes with acceptable accuracy. Static and dynamic data collection is performed in a laboratory environment and multiple Bus Rapid Transit (BRT) lanes, respectively. The camera captures the projected laser line on the pavement surface, and this image is processed by applying image quality enhancement filters. A clustering method is used to detect the borders of ruts. Moreover, the effect of light level and the distance from the defect surface are measured. The results illustrate that the system has its best performance in 0.0001 lx light level with a relative error of 4%. Based on the paired sample t-test outputs, the optimal distance between the sensors and the ground should be 1 m. By validating the outputs of the system with the standard measurement method, the results show that this system is capable of computing the rut depth between 0.05 and 5.5 cm with a relative error of 9.31%, which confirms its capability for accurate data collection. Moreover, the replicability tests confirm the capability of using this system as an accurate data collection tool.

Educational trips are one of the main parts of urban trips. It includes school and university trips. University students are a social group with complicated and unique travel behaviour. They have variable class schedules and considerable freedom in the campus environment. Therefore, planning university students’ trip is a vital part of urban planning, especially if the university is located in CBD area This study examined activities of 1049 students of Amirkabir University of Technology (AUT). Participants completed a travel diary in a typical school day during a term. Different statistical analysis was adapted to the survey data including demographic and travel characteristics. Other than descriptive analysis on data, a Poisson regression model and a linear regression model has been developed on students’ number of activities in a day. As AUT is located in CBD area of Tehran and due to two different traffic restrictions for private cars in this area the study confirmed that walking and public transit are the dominant modes among AUT students and only 4% of students are using private cars The model demonstrates that off-campus students are more likely to have more activities compared to on-campus students. In addition, the insights gained from this study will serve as the basis of an activity-based university travel demand model for AUT.

On-time maintenance of deteriorated pavement is a crucial step toward minimizing its life cycle costs. Maintenance and rehabilitation actions are planned based on pavement condition data including road roughness. The condition of low volume roads is not monitored frequently due to the high cost of data collection and their less priority. However, road users traverse them widely. The objective of this study is to develop an inexpensive data acquisition system based on the use of a Sharp IR-based sensor and an accelerometer to capture pavement profile and estimate its roughness index with adequate accuracy for low volume roads. Design of study, data management, data acquisition, and data analysis are the steps of this research. The results demonstrate that the developed system is able to measure pavement roughness of uneven roads with 87.4% accuracy at 30 km/h speed. Therefore, it can be a good alternative for expensive data collection vehicles.

In this study, an acoustical-based macrotexture measurement method that uses the friction-induced mechanism of tire/road noise has been proposed. This mechanism influences the frequencies above 1 KHz and is the dominant mechanism for frequencies above 3 KHz. The airflow due to this mechanism passes through the macrotexture which can be represented using convolution in the time-domain of the airflow, which is viewed as the input signal, and macrotexture, which is considered as the transmission channel. Using the Cepstral processing method, the related features to macrotexture airflow can be extracted and the macrotexture can be evaluated. To investigate the proposed method, interaction noise was measured for six uniform non-porous asphaltic test tracks with different macrotextures. The tire/road noise signal is passed through a bandpass filter (3–5 KHz). By liftering the cepstrum components in the range of 2–50, the feature vector has been obtained and then used as input to a multiclass SVM, resulting in a precision error of 4%.

The purpose of this study was to assess the geometrical quality of railway turnouts through application of power spectral density (PSD). PSD in the form of a continuous curve can show turnout irregularities by means of wavelength and amplitude. As a case study, the data related to longitudinal level parameter measurements for four turnouts in the Swedish Railways from 2007 to 2011 were acquired. The PSD curves for these measurements were calculated by means of ProVal software. The PSD curves were compared by PSD standard in a program written in MATLAB and turnout irregularities were determined. The results show that PSD is able to demonstrate the condition of turnouts individually and can also identify the wavelength faults present in different sections of a turnout. More important, PSD can be applied to prioritize maintenance needs of turnouts on the basis of their criticality. Use of PSD can help identify tamping needs more precisely. As a result, the geometrical quality can be restored before the defined maintenance thresholds are exceeded. This maintenance action also would reduce the total downtime, obligatory speed reductions, and maintenance costs.

Pervious concrete is a sustainable pavement with high permeability. The purpose of this study is to evaluate physical and mechanical properties of the pervious concrete including density, strength, porosity, and permeability. Taguchi design of experiments was used to optimize the performance of these characteristics. The relationship between properties dependent on coarse aggregate size. As the maximum size of the coarse aggregate increases, both the permeability and porosity grows up. Also, it results in a significant decrease in compressive strength. There is a trade-off between strength and permeability which should be considered to meet the minimum requirements for the pervious concrete.

Pavement roughness and surface distress detection is of interest of decision makers due to vehicle safety, user satisfaction, and cost saving. Data collection, as a core of pavement management systems, is required for these detections. There are two major types of data collection: traditional/manual data collection and automated/semi-automated data collection. This paper study different non-destructive tools in detecting cracks and potholes. For this purpose, automated data collection tools, which have been utilized recently are discussed and their applications are criticized. The main issue is the significant amount of money as a capital investment needed to buy the vehicle. The main scope of this paper is to study the approach and related tools that not only are cost-effective but also precise and accurate. The new sensor called Kinect has all of these specifications. It can capture both RGB images and depth which are of significant use in measuring cracks and potholes. This sensor is able to take image of surfaces with adequate resolution to detect cracks along with measurement of distance between sensor and obstacles in front of it which results in depth of defects. This technology has been very recently studied by few researchers in different fields of studies such as project management, biomedical engineering, etc. Pavement management has not paid enough attention to use of Kinect in monitoring and detecting distresses. This paper is aimed at providing a thorough literature review on usage of Kinect in pavement management and finally proposing the best approach which is cost-effective and precise.

This paper examines the application of power spectral density (PSD) in turnout geometrical degradation analysis. Power spectral density in the form of a continuous curve can show track irre...

Data collection and the associated automated methods are one of the main building blocks of pavement management systems. One of the main problems associated with automated data collection methods is the high cost of the equipment involved. The aim of this research is to develop a cost-effective system for dense-graded asphalt pavement macrotexture monitoring. To this end, a system based on the tire/road noise, utilizing microphones and employing cepstral signal processing has been developed. The proposed method is compared with the current state of the art PCA based method and is shown that the precision error of the proposed method is about 7%, which outperforms the previous state of the art. To enhance the precision of the cepstral method for the vehicle speed variation amongst the collected dataset, the combination of the cepstral signal processing with Gaussian mixture models is proposed which results in the final precision error of 8%.

Pavement management systems have become more crucial through the development of road networks. Data collection is at the core of any pavement management system. Rutting is a critical defect...

Railway transportation has a significant advantage compared to other modes of transportation due to its high level of safety, significant capacity, and environmentally friendly nature, lead...

Pavement management encompasses a wide range of tasks from data collection and data processing to maintenance management and life cycle cost analysis. This study concentrates on maintenance management at a project level and a network level. A decision support system provides optimum maintenance actions over time to enhance the performance of a pavement network and prolong its life span. A heuristic method i.e., Genetic Algorithm is applied to tackle this optimization problem. Due to complexity of the problem, applying an optimum Genetic Algorithm structure results in significant enhancements in the Genetic Algorithm procedure and saves computation time which has not been received enough attention to date by researchers. An experimental design is conducted to investigate the optimum Genetic Algorithm structure for solving a pavement maintenance problem. Since the current Genetic Algorithm software is not suitable to run the experiment, an accurate spreadsheet program has been developed for this purpose to conduct the experiment. Two types of objective functions have been applied: single objective functions (minimizing cost) vs. multiple objective functions (minimizing cost and maximizing benefit).;

Pervious concrete pavement (PCP) is of significant importance in the field of stormwater management in terms of reducing runoff volume. Stormwater managers should initially ensure that PCP adequately performs over time to be able to implement it in a stormwater management system. Performance models are intended to predict the performance of an asset over its service life. To develop a performance model commonly long-term performance data are essential. No performance model has been developed for PCP to date because PCP long-term performance data are rarely available. In such a case, expert knowledge is an alternative method to collect data for developing a performance model. This research aims to develop performance models for PCP for the first time by using an integrated Markov chain technique (combination of homogenous and non-homogenous techniques) through incorporation of expert knowledge. Both deterministic and stochastic approaches are applied to build up Markov models by using expected values and the Latin hypercube simulation technique, respectively. Both approaches provide consistent results although the stochastic Markov model provides more detailed results. Short-term experimental field data are also incorporated to validate the Markov performance models.