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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.

Pavement is one of the main elements of the roads network. It is extremely essential to study and understand the factors affecting its performance and highlight the most important ones for decision-makers and pavement experts to consider during the design, construction and maintenance stages. The purpose of this paper was to identify the factors affecting pavement performance and rank them according to their importance using Analytic Hierarchy Process (AHP) for decision-makers and pavement experts to consider during the design, construction and maintenance stages.

A survey was developed considering 29 factors found in the literature that affect pavement performance. The survey was sent to pavement professionals in Qatar to rate their perception of factors affecting pavement performance to enhance roads' sustainability. 205 responses were collected and analyzed using AHP.

The findings indicate that the factor “unconsidered heavy vehicles volume” is the most critical factor that affects pavement performance. The second most critical factor affecting the pavement performance is the “low asphalt content” due to escalating binder aging, reducing fatigue life of the pavement and decreasing the durability of roads. The third and fourth factors are “poor mechanical and thermal properties” and “unexpected high traffic volume,” respectively. These two factors are strongly attached to the first and second factors since the traffic volume affects the pavement performance less but similar to the heavy vehicles and a mix with poor mechanical and thermal properties is related indirectly to the asphalt content in the mix.

The research provides help for decision-makers in the construction industry to improve the performance of pavements using a multi-criteria decision-making tool. This paper's outcome would help the pavement management professionals in the construction industry to improve pavement performance and management, increase the pavement's life cycle and reduce maintenance costs.

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.

Various approaches have emerged to assist practitioners in making more informed decisions in highway construction projects. However, industry practitioners are still using subjective ways to make decisions. Also, researchers have developed tools and techniques with similar objectives. Lack of information on what has been developed might lead to those issues. Therefore, this paper aims to review trends of evolution, pinpoint strengths and gaps in the literature and identifies potential future directions for decision-making research in highway construction projects.

A systematic review was conducted on published articles on decision-making in highway construction projects using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique.

The analysis of 101 articles revealed that existing decision-making research in highway construction projects targets improvements in four areas: feasibility, conceptual, detailed scope and detailed design. The four areas consist of sixteen subthemes that are detailed in this study. In addition, most research involved developing decision support tools and systems as well as decision-making models, techniques and frameworks. Lastly, several research areas have emerged, such as adding more decision criteria including those with uncertainties, expanding existing decision-making models into decision support systems, benchmarking decision criteria between different sample populations and exploring inter-and intra-relationships between decision criteria.

This paper provides an overview of existing research on decision-making in highway construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research. Finally, industry practitioners can use the findings to develop strategies for effective decision-making processes.

This paper aims to provide an integrated framework of life cycle assessment (LCA) and life cycle cost analysis (LCCA) for assessing alternative technologies, processes, and/or activities, with focus on concrete pavements.

LCA and LCCA are used to evaluate environmental and economic impacts of substituting different percentages of fly ash and slag into continuously reinforced concrete pavement (CRCP) and jointed plane concrete pavement (JPCP). Impacts are determined over different life cycle phases.

An LCA of the extraction phase indicated that JPCP pavement had 33‐62 percent less emissions than CRCP pavements, when only steel consumption was considered. When cement was considered, JPCP pavement had almost 40 percent greater emissions then CRCP for all mix types. An LCCA showed that over the entire life cycle phases studied, CRCP pavements had about 46 percent more costs than JPCP. However, when only maintenance costs were considered, CRCP pavement cost 80 percent less to maintain than JPCP over the studied period of 35 years.

The study is a step towards using an integrated framework to evaluate the performance of different materials and technology. The same framework could be conducted for different kinds of asphalt pavements and concrete pavements, as well as other infrastructure that makes up the built environment, with the goal of making decisions that take into account design considerations, environmental impacts, and cost effectiveness.

Highways are one of the most critical infrastructure projects with strategic impact on the countries’ development. Asphalt has been historically the main pavement material used in all highway projects, especially in Egypt. However, with the booming in concrete technology in the past two decades, concrete has become a strong rival to asphalt, especially in highway applications. Several factors impact the decision-making criteria for any highway, which differ according to the priorities and requirements of each decision-maker and the nature of the project.

This research focuses on studying and analyzing the different factors that impact the decision for selecting the material type for highways in Egypt’s pavement construction industry. The outputs of the analysis are then incorporated into a multi-decision-making tool to assess the optimum solution as per the priorities of the decision-maker. A holistic framework is developed to compare asphalt and concrete pavements solutions considering; initial cost, maintenance cost on the life cycle, construction duration, embodied energy and fuel consumption. The data collection on local highways was performed through interviewing and surveying experts in the consulting, contracting and building materials fields (total of 15 respondents).

A multi-decision-making tool developed using the superiority and inferiority ranking method for selecting the best alternate. To illustrate the practicality of the proposed framework, a case study for assessment and validation has been done on Sokhna–Quarries highway in Egypt. The framework results reveal that despite a lower initial cost and faster construction of asphalt, concrete pavement is more cost-efficient on the lifecycle time. The multi-decision-making model indicates that concrete is a better alternate for highway applications given the cost, time and energy factors considered.

The proposed model takes into consideration the important parameters in selecting the type of pavement to be constructed considering two alternates asphalt and concrete.

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.

The purpose of this study is to investigate the effect of various heat conditions on the durability of eggshell powder (ESP)–flyash (FA) geopolymer subjected to wetting–drying cycles.

In this study, two waste materials, ESP and FA, which are destined for landfills, were used as precursors to produce geopolymers in a sustainable manner. The mixture of Na₂SiO₃ and NaOH was used as a liquid alkaline activator in geopolymerization. The ESP and FA content were varied as 30, 50 and 70% and Na₂SiO₃/NaOH ratios were varied as 0.5, 1 and 2. Geopolymer samples were cured at three heat conditions: 25°C (ambient temperature), 50°C and 80°C for seven days prior to durability tests.

The results of this study revealed that the strength loss of the geopolymer decreases with an increase in curing temperature up to 50°C and then increases for higher temperature up to 80°C. Further, the strength loss of the geopolymer decreases with an increase in FA replacement and Na₂SiO₃/NaOH ratio. Geopolymer composites exhibited early strength development because of the inclusion of calcium-rich ESP. The weight loss of the ESP–FA geopolymer follows a similar pattern of strength loss. Geopolymer samples previously cured at optimum heat condition of 50°C for seven days exhibited higher durability.

The inclusion of calcium-rich ESP in FA-based geopolymer is novel research. As ESP–FA geopolymer composites show higher mechanical strength and higher durability compared to Indian standards, the potential use of this geopolymer can be in road subbases/subgrades.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.