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Pavement deterioration prediction models play a crucial role in determining maintenance strategies and future funding needs. While deterioration prediction models have been studied extensively in the past, applications of these models to local street networks have been limited. This study aims to address this gap by sharing the results of network level deterioration prediction models developed at a local level.

Network level pavement deterioration prediction models are developed using Markov chains for the local street network in Syracuse, New York, based on pavement condition rating data collected over a 15-year time period. Transition probability matrices are generated by calculating the percentage of street sections that transition from one state to another within one duty cycle. Bootstrap sampling with replacement is used to numerically generate 95% confidence intervals around the transition probability values.

The overall local street network is divided into three cohorts based on street type (i.e. avenues, streets and roads) and two cohorts based on pavement type. All cohorts demonstrated very similar deterioration trends, indicating the existence of a fast-paced deterioration mechanism for the local street network of Syracuse.

This study contributes to the body of knowledge in deterioration modeling of local street networks, especially in the absence of key predictor variables. Furthermore, this study introduces the use of bootstrap sampling with replacement method in generating confidence intervals for transition probability values.

A large number of roads have been constructed in the rural areas of India to connect habitations with the nearest major roads. With time, the pavements of these roads have deteriorated and they need some kind of maintenance, although they all do not need maintenance at the same time, as they have all not deteriorated to the same level. Hence, they have to be prioritized for maintenance.

In order to present a scientific methodology for prioritizing pavement maintenance, the factors affecting prioritization and the relative importance of each were identified through an expert survey. Analytic Hierarchy Process (AHP) was used to scientifically establish weight (importance) of each factor based on its relative importance over other factors. The proposed methodology was validated through a case study of 203 low volume rural roads in the state of Himachal Pradesh in India. Ranking of these roads in order of their priority for maintenance was presented as the final result.

The results show that pavement distresses, traffic volume, type of connectivity and the socioeconomic facilities located along a road are the four major factors to be considered in determining the priority of a road for maintenance.

The methodology provides a comprehensive, scientific and socially responsible pavement maintenance prioritization method which will automatically select roads for maintenance without any bias.

Timely maintenance of roads will also save budgetary expenditure of restoration/reconstruction, leading to enhancement of road service life. The government will not only save money but also provide timely benefit to the needy population.

Road transportation is the primary mode of inland transportation in rural areas. Timely maintenance of the pavements will be of great help to the socioeconomic development of rural areas.

The proposed methodology lays special emphasis on rural roads which are small in length, but large in number. Instead of random, a scientific method for selection of roads for maintenance will be of great help to the public works department for better management of rural road network.

The primary function of pavement is to distribute the concentrated loads so that supporting capacity of the sub-grade soil is not exceeded. Due to continuous application of impact loading, pavement surface is often subjected to various types of distresses. The study was conducted on a portion (Madina Market to Tuker Bazar) of Sylhet-Sunamganj road that acts as a very important link between two adjacent towns. Pavement condition ratings (PCR) of the pavement was obtained on the basis of severity and extent of different types of distresses such as alligator cracks, longitudinal cracks, potholes, rutting, raveling, patching etc. The PCR values of different segments of ‘Madina Market-Tuker Bazar’ were evaluated in this study most of which fall within the category of ‘Very Poor to Poor’ conditions. Such condition of PCR immediately recommends proper treatment of the pavement for usage convenience.

Soil is an essential component of road construction and is used in the form of subgrade materials. It ensures the stability and durability of the road under adverse conditions; being one of the important parameters, poor judgment of the engineering properties of soil can lead to pavement failure. Geopathic stress (GS) is a subtle energy in the form of harmful electromagnetic radiation. This study aims to investigate the effect of GS on soil and concrete.

A total of 23 soil samples from stress zones and nonstress zones were tested for different engineering properties like water content, liquid limit, plastic limit, specific gravity and California bearing ratio. Two concrete panels were placed on GS zones, and their quality was monitored through nondestructive testing for a period of one year.

The result shows that the engineering properties of soil and pavement thickness are increasing in stress zones as compared with nonstress zones. For concrete panels, as time passes, the quality of the concrete gets reduced, which hints toward the detrimental effect of GS.

This research is a systematic, scientific, reliable study which evaluated subgrade characteristics thus determining the detrimental impact of the GS on soil and pavement thickness. On a concluding note, this study provides a detailed insight into the performance of the road segment when subjected to GS. Through this investigation, it is recommended that GS should be considered in the design of roads.

Airport construction is extremely different than the construction of any other transport infrastructure. It is a vital area affecting both the environment and the economy. Therefore, sustainable selection of building materials represents a very important step. The main objective of this paper was to develop a set of sustainable assessment criteria (SAC) to assist design team members in the selection between two different material alternatives when constructing a new runway in Kuwait international airport. The proposed materials were asphalt or concrete.

A set of 24 sub-criteria were developed, those were emerged from three main criteria: technical, environmental and socio-economic. Fuzzy analytical hierarchy process (FAHP) was employed to assign weights and to measure the relative importance of these criteria for the material alternatives selection. The prioritizing process for criteria was based on a survey of 100 responses.

It had been concluded from the use of FAHP that asphalt material was a better alternative in maintainability, ease of construction, health and safety, initial cost and energy saving. Concrete was better in fire resistance, durability, decay resistance, energy saving and thermal insulation, maintenance cost, aesthetics, minimizing pollution, impact on air quality, low toxicity, environmentally disposal sound system, amount of likely wastage and raw material extraction method.

Develop a set of SAC to assist design team members in deciding between two material alternatives to construct a runway.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

Police organizations' main goal is providing the security of the neighborhoods for citizens. This paper aims to investigate how efficiently the police stations in the city of Ankara work. It also seeks to determine how the efficiency of police organizations can be measured by using data envelopment analysis (DEA).

The study uses DEA to measure the efficiency of police stations in the city of Ankara in Turkey. DEA measures the relative efficiency of decision‐making units (DMUs) based on their inputs and outputs.

The results showed that ten of 19 police stations in Ankara were efficient. In other words, 52.6 percent of police stations were found to be efficient.

All police stations in Ankara could not be included in the study. Since DEA analysis the relative efficiency of DMUs, including all police stations in Ankara, may have given a broader perspective for police managers.

Inefficient police stations in this study should either reduce the number of incidents that occurred in the precinct or solve those crimes and increase the number of solved incidents. Preventing crimes may be achieved by employing community policing strategies, thus, police managers should focus on adopting community policing strategies.

The results of the DEA analysis can be used to help police stations in Ankara to be more efficient. Furthermore, applying DEA may be a new approach to measuring the efficiency of police services.

The purpose of this study is the development of an objective approach to prioritize and rank airfield pavement sections based on their condition and justify their funding requirements using a soft-computing technique.

The airfield pavement condition is evaluated by collecting data through field tests and visual surveys. The performance indicators are selected as deflection, structural index, subgrade modulus and pavement condition index, by taking the help of field experts. The condition of pavement sections is analyzed by obtaining scores for each sections using Buckley’s fuzzy analytic hierarchy process. The sections are finally ranked for performing their maintenance and repair activities.

The condition of pavements is represented using a single score that takes an account of cumulative impact of various parameters as well as any subjectivity associated with human perceptions.

The developed methodology is very useful for its practical implications, and it is explained using a case study of an international airport.

Decision-making for maintenance and repair practices is often based on subjective decisions and lacks a robust and judicious approach. Thus, obtaining sufficient budget for repair and maintenance becomes one of the primary challenges. This study adds a value to prevailing practices by developing an objective decision-making methodology. Additionally, the use of non-destructive testing techniques, which pose little or no necessity to destructive coring and boring, eases this task.

This paper describes the use of an analytical hierarchy process (AHP) in determining the rational weights of importance of pavement maintenance priority ranking factors. These weights were obtained by capturing the local people’s perception towards this vital part of the pavement management system (PMS). In this regard, different groups of individuals were asked to estimate the weight of importance in pavement maintenance of different factors for ranking pavement sections. These factors were road class, pavement condition, operating traffic, riding quality, safety condition, maintenance cost, and the overall importance of the road section to the community. The AHP method of pair‐wise comparison was employed to get the factor weights, which were compared with the weights obtained from the direct assignment method. It was concluded that the two methods were statistically similar which confirms that the results of the direct assignment method can be used safely with a sound reliability and consistency. This conclusion comes from the fact that the AHP method has a high reputation and applications, and it uses a high‐precision technique for obtaining the weights (priorities) of alternatives or items. Priority factor weights were used in developing a pavement maintenance priority ranking procedure for a road network. This procedure was validated by real case studies, and found to be logically and efficiently able to handle the ranking of a huge number of pavement sections for maintenance and repair.

With the majority of highway projects in China having entered their operational phases, the maintenance and repair of the pavement is receiving increasing attention. One problem that needs to be addressed urgently is that of how to raise the proper funds for highway maintenance to ensure the sustainable operation of the project. To this end, the aim of this study is to investigate the capital demand for operation and maintenance of a project by means of a refinancing scheme, in order to reduce the possibility of project bankruptcy and to enhance the economic value of the project.

Based on an analysis of the dynamic complexity of the highway pavement maintenance system, a Markov model is used to predict pavement performance, and an optimal capital structure decision model is proposed for highway public–private partnership (PPP) project refinancing, using the method of system dynamics (SD). The proposed model is then applied to a real case study.

Results show that the proposed model can be used to predict accurately the dynamic changes in the demand for road maintenance funds and refinancing during the period of operation, before making the optimal decision for the refinancing capital structure.

Although many scholars have studied the optimal refinancing capital structure of PPP projects, the dynamic changes inherent in the demand for maintenance funds for highway PPP projects are seldom considered. Therefore, in the approach used here the influence of the dynamic change of road maintenance capital demand on refinancing is investigated, and SD is used for the optimal capital structure decision-making model of highway PPP project refinancing, to make the decision-making process more reasonable and scientific.