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Prediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.

The 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).

Classification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.

The proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.

This study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.

This study aims to reveal residents’ individual perceptions of nature-based destination development and preferences for infrastructure and tourism superstructure development among communities that rely heavily on wildlife tourism.

A discrete choice experiment (DCE) was used among the Maasai community based in the villages and towns near the Maasai Mara National Reserve in Kenya. The attributes included type of tourism accommodation, location of tourism accommodations, types of access roads (tarmac or marram), tourist numbers and desired land-use options (between tourism development, livestock grazing and agriculture). A DCE analysis with hierarchical Bayes estimation was performed.

It revealed that the introduction to land-use restrictions and the location of tourism accommodations were the most important attributes for the respondents, with average importance values of 30.36% and 24.02%, respectively. A significant less important attribute was the types of access roads with an average importance of just 8.38%. Cluster analysis revealed widespread heterogeneity in preferences.

The survey-based DCE was conducted in the Maasai Mara National Reserve, Kenya, and therefore may not be relevant in other contexts. The focus was also only on the residents’ preferences. The findings broaden the knowledge on tourism developments and residents’ support for development and management of protected areas.

For policymakers, conservation practitioners and tourism businesses, this study provides a source of reference for understanding the development preferences of the Maasai community. In general, the study contributes to a better understanding of local communities in relations to tourism development and residents’ support for developments and management of protected areas (PAs).

This study fills the gap in the literature on tourism development and residents’ support for developments in PAs by presenting some limits of acceptable and desirable use of PAs among a community that has a complex coexistence with a wildlife tourism destination. It provides an alternative perspective for future research by examining residents’ choice towards destination development and preferences for infrastructure and tourism superstructure development using an experimental approach.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

Several studies were made on paired site and soil–structure interaction (SSI) effects, but most of them were site specific. This paper aims to investigate the impact of SSI effects in conjunction with local soil condition effects on the seismic response of typical multistory low- to mid-rise–reinforced concrete (RC) buildings resting on Algerian regulatory design sites through a global explicit transfer function (TF).

A preliminary quantification of SSI effects associated with site effects is carried out through a frequency-domain solution based on the concept of rock-to-soil surface displacement TF performed for each design site category. It results from the combination of the TFs of structure, foundation and soil and reflects how seismic waves are amplified due to changes in the geological contrast between the rock and overlying soil deposits. As well, response modification factors, denoting displacement ratios of the building responses within the flexible and site-structure conditions with respect to the fixed-base one, are carried out.

In the context of Algerian seismic regulation, the study provides a clear vision of how and when site or SSI effects are expected to be influential, as opposed to the fixed-base hypothesis still retained by the current regulation. This helps engineers to be aware of the extent of the expected seismic damage.

The research applies to low- to mid-rise RC buildings within the Algerian seismic regulation, but it may also be expanded to other examples that fall under other seismic regulations.

The response modification ratio is a quantitative approach to assessing response fluctuations. It draws attention to how the roof level drift varies depending on the condition. These results can be used as numerical parameters in structural seismic design when the structure is comparable because they provide useful information about how the two phenomena interact with the structure.

The study goes beyond particular situations dealing with site specific and offers effective indicators and quantitative evaluation of combined site and SSI effects according to the current national seismic provisions, where no indication about site or SSI effects exists.

After COVID-19, the tourism industry has been reshaped through a cycle of intertwined crises. So, the tourism industry has been the scene of discussions on how to proceed against vulnerabilities and uncertainties in recent years. Destinations have begun to think differently in terms of competitive advantage in order to survive. Therefore, the success of the tourism industry needs a change in mindset in order to keep up with the transformation accelerated by COVID-19. Regenerative tourism has triggered the search for a different mindset beyond sustainable tourism efforts by addressing responsibility and creativity together. As a result of these searches, regenerative tourism reveals a philosophical perspective as a mindset. Most of the studies on regenerative tourism contain conceptual clarification efforts. However, there is a need for a macro evaluation of regenerative tourism management on a destination basis. This chapter discusses the design of an appropriate policy and planning for the management of regenerative tourism. Regenerative tourism emphasizes a conscious intervention in the tourism industry with its creativity and innovative and responsible characteristics. This intervention can be evaluated within the scope of destination policy and planning for solving the problems about standardization and commercialization. Thus, it will conceptually be tried to put forward a planning and policymaking process that is suitable for regenerative tourism.

As an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.

First of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.

The feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.

This paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

To conduct a cross-border transaction, participants need an exchange object (goods) and at least two direct parties (the seller and the buyer), forming the foundation of a significant exchange mechanism. Beyond the primary buyer and seller, the transaction involves intermediaries, representatives of actors, carriers, insurers, state officials, and other individuals. These stakeholders engage at various levels, ranging from international and national to regional and local communities. The authors propose a novel way to classify stakeholders based on their involvement in value-added distribution. Trading operations enable actors to gain a trading margin; many stakeholders participate in this process. One group of stakeholders contributes to shaping the “rules of the game,” establishing framework conditions and limitations for cross-border commodity exchange. Another larger group experiences the direct or indirect impact of export–import operations on their activities. This distinction among stakeholders leads the authors to develop a two-tier model for trade relations in border regions. The model differentiates between exogenous and endogenous factors influencing the dynamics of the resulting features, making it possible to examine border regions' development under the influence of cross-border trade dynamics.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

This research aims to reveal the role of sustainability in tourism in terms of smart technologies by addressing the relationship between smart tourism and sustainability through a thematic literature analysis.

The research is based on examining the role of smart tourism in terms of sustainability within the studies in the literature.

Tourism is a mass-movement industry that uses natural and artificial resources intensively. For this reason, intensive studies are carried out on this industry in sustainable studies. Based on smart cities, smart destinations have started to be created to focus on specific tourism activities, balance the use of resources in this field, ensure the welfare of tourists and local people, and, most importantly, create sustainable activities. These technology-based cities can save many resources that people need to consider. In addition, thanks to its integration with technological devices, it enables people to be much more effective in social and business life and to act more practically in terms of time, distance, and communication. Besides the stakeholders within the destination, tourists visiting the destination can have a more comfortable and resource-efficient holiday experience thanks to the technological applications and systems created here.

The research presents a thematic literature review on the importance of sustainability in the sector within the framework of smart tourism practices.