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This paper aims to describe and analyse the community’s perceptions of buren sites and how they maintain their value. Buren is a term used by locals to refer to the location of former iron ore smelting in the past. Archaeological findings at smelting sites are slags, ore fragments, tuyeres and smelting furnaces. These sites urgently need to be conserved because the expansion of oil palm plantations and coal mines is ongoing. Buren sites are on community and customary land, so site conservation and development must consider community perception.

The primary data were gathered through interviews, group discussions and observation. A literature review of previous research results obtained secondary data. Interviews and group discussions were digitally recorded and transcribed verbatim as qualitative coding. Thematic content analysis was performed using the concepts of the community’s perception and heritage values.

The community initially did not care about the buren. Today, they believe the buren as ancestral heritage has historical and educational value. The community hopes the buren can be preserved and developed to reinforce identity and pride as Dayak people and may improve community welfare.

Preserving and developing buren sites necessitate serious planning involving multiple stakeholders, as well as incorporating buren sites into development plans and regional socioeconomic growth. Future research should focus on strategies for preserving and developing buren sites.

Research results are used to teach students about the history and technology of metal making, instilling a sense of patriotism, strength and pride in Dayak’s identity.

The research has an impact on attitudes and behaviour of the locals. Previously, locals did not care about the buren tend to be afraid to enter it because they believed that buren was a guarded spirit. Now, the community has become concerned, participating in preserving the buren site as providing temporary roofs and making signboards and roads to the site. The community with support from regional officials expects to conserve and utilise the buren site as a tourist destination, combining river tours and forest tourism to create economic opportunities for local tour guides, parking services and boat providers.

The authenticity of this research is the sites are archaeologically valuable and are threatened by the expansion of oil palm plantations and coal mining. This is also the first study of an iron industrial site in Indonesia that addresses this problem and uses this method.

Drawing motivation from the greater exposure to uncertainty and condition changes that affect large projects due to their long lifecycle, this paper aims to investigate how the time factor affects the use of governance mechanisms to pursue the success of these projects.

To pursue its aim, the article applies the dichotomization between the hard and soft mechanisms of project governance to the analysis of a historical case study, whose findings are organized over the short, medium and long periods. The case selected is referred to the peculiar water system, made up of tunnels named “bottini,” that was in use in Siena (Italy) as the old aqueduct. Specifically, the study focuses on the project of expansion of this water system that was realized during the 14th century for the construction of the “Bottino maestro di Fontegaia.”

This article highlights the different relevance that, during the lifecycle of large projects, is assumed by hard and soft governance mechanisms, with the former having main relevance in a short and medium period, and the latter usually emerging in the medium period and, subsequently, playing a growing role for the project success in the long period.

The article contributes to the literature on large projects by providing novel insights about how the time factor impacts the governance of these projects. Furthermore, the case study, with its unique history, highlights the relevance of combining effectively the hard and the soft dimensions of project governance to pursue success.

Engaging with heritage to support mental health and wellbeing has become a focus of research and policy, more recently moving towards social prescription of heritage interventions. While there are benefits to active participation, there are potential risks to those taking part and to the non-renewable historic remains and landscape that form the core of these projects. The purpose of the current research paper was to develop best practice guidelines for organisations offering heritage projects as interventions for people who live with mental health issues to protect both participants and heritage.

There were two research phases; a Sandpit with World Café discussions to produce a set of research priorities, and a Delphi Consultation, using three questionnaires distributed over six months, to develop best practice guidelines. The panel in both phases comprised experts through lived experience, policy, practice and research.

The Authentic and Meaningful Participation in Heritage or Related Activities (AMPHORA) guidelines cover three stages: project development, project delivery and project follow-up, with a set of action points for each stage. Of particular importance was authentic participation and expertise to ensure appropriate management of heritage/ historic environment assets and support for participants.

The AMPHORA guidelines can assist all organisations in the delivery of safe projects that support the mental health of those involved, as well as enhancing and protecting the historic environment.

To the best of the authors’ knowledge, these are the first research-led guidelines that help heritage organisations support those living with mental health issues.

The purpose of this paper is the dynamic analysis and seismic damage assessment of steel sheet pile quay wall with inelastic behavior underground motions using several accelerograms.

Finite element analysis is conducted using the Plaxis 2D software to generate the numerical model of quay wall. The extension of berth 25 at the port of Bejaia, located in northeastern Algeria, represents a case study. Incremental dynamic analyses are carried out to examine variation of the main response parameters under seismic excitations with increasing Peak ground acceleration (PGA) levels. Two global damage indices based on the safety factor and bending moment are introduced to assess the relationship between PGA and the damage levels.

The results obtained indicate that the sheet pile quay wall can safely withstand seismic loads up to PGAs of 0.35 g and that above 0.45 g, care should be taken with the risk of reaching the ultimate moment capacity of the steel sheet pile. However, for PGAs greater than 0.5 g, it was clearly demonstrated that the excessive deformations with material are likely to occur in the soil layers and in the structural elements.

The main contribution of the present work is a new double seismic damage index for a steel sheet pile supported quay wharf. The numerical modeling is first validated in the static case. Then, the results obtained by performing several incremental dynamic analyses are exploited to evaluate the degradation of the soil safety factor and the seismic capacity of the pile sheet wall. Computed values of the proposed damage indices of the considered quay wharf are a practical helping tool for decision-making regarding the seismic safety of the structure.

Among the various applications involving the use of microwave energy, its growing utility within the mining industry is particularly noteworthy. Conventional grinding processes are often overburdened by energy inefficiencies that are directly related to machine wear, pollution and rising project costs. In this work, we numerically investigate the effects of microwave pretreatment through a series of compression tests as a means to help mitigate these energy inefficiencies.

We investigate the effects of microwave pretreatment on various rock samples, as quantified by uniaxial compression tests. In particular, we assign sample heterogeneity based on a Gaussian statistical distribution and invoke a damage model for elemental tensile and compressive stresses based on the maximum tensile stress and the Mohr–Coulomb theories, respectively. We further couple the electromagnetic, thermal and solid displacement relations using finite element modeling.

(1) Increased power intensity during microwave pretreatment results in decreased axial compressive stress. (2) Leveraging statistics to induce variable compressive and tensile strength can greatly facilitate sample heterogeneity and prove necessary for damage modeling. (3) There exists a nonlinear trend to the reduction in smax with increasing power levels, implying an optimum energy output efficiency to create the maximum degradation-power cost relationship.

Previous research in this area has been largely limited to two-dimensional thermo-electric models. The onset of high-performance computing has allowed for the development of high-fidelity, three-dimensional models with coupled equations for electromagnetics, heat transfer and solid mechanics.

This study aims to investigate ground-related design deficiencies as potential avenues of avoidable cost overruns, discernible from the geotechnical practices of highway agencies in the Niger Delta region of Nigeria.

The study deploys an interpretivist qualitative methodology to provide a detailed descriptive analysis of the design-related geotechnical practices of highway agencies during the pre-contract phase of highway projects. Semi-structured interviews were conducted with in-house professionals, consultants and contractors affiliated with the three highway agencies in the Niger Delta and thematically analysed to identify significant deviations from geotechnical best practices.

The study outcome shows that during the pre-contract phase, a chain of design-related geotechnical shortcomings has plagued highway projects executed in the Niger Delta. This view of practice uncovered in this study demonstrates a culture of significant deviation from best practice recommendations, which could plausibly contribute to the history of significant project cost overruns recorded in the region.

The study qualitatively spotlights gaps in the practice of highway agencies and reinforces the need for a re-orientation of the attitude to risk management, to give geotechnical concerns a priority in the financial management of highway projects executed in the Niger Delta region of Nigeria.

This study was conducted with the main purpose of recording primary data related to environmental factors, which has become the main criteria in the selection of the Sungai Batu Archaeological Complex (SBAC) as the center of the iron smelting industry and trade in ancient Kedah.

To fulfill this purpose, field studies involving drone photogrammetry mapping, augering, core drilling and geophysical mapping methods were carried out.

The results obtained through the application of the method have shown that SBAC has a good environment, which has a wide and deep river flow, the existence of Mount Jerai and the abundance of iron ores, mangrove Merbok and clay.

Resources did not allow for environment studies of the by-products tourism sites as part of the current study.

The study also included a survey and mapping to obtain potential primary data around SBAC in the process of developing it as the center of the world iron industry.

One finding is that attention to heritage policy and protection must be ongoing at all levels of government and the local community to ensure that the survey and mapping data carried out can be developed as a sustainable heritage tourism product.

This study reveals primary data related to the suitability of paleoenvironment in the SBAC development process as a world iron smelting industry area.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Ores and minerals are extracted from the earth’s crust depending on the type of deposit. Iron ore mines come under massive deposit patterns and have their own mine development and life cycles. This study aims to depict the development and life cycle of large open-pit iron ore mines and the intertwined organizational design of the departments/sections operated within the industry.

Primary data were collected on the site by participant observation, in-depth interviews of the field staff and executives, and field notes. Secondary data were collected from the literature review to compare and cite similar or previous studies on each mining activity. Finally, interactions were conducted with academic experts and top field executives to validate the findings. An organizational ethnography methodology was employed to study and analyse four large-scale iron ore mines of India’s largest iron-producing state, Odisha, from January to April 2023.

Six stages were observed for development and life cycle, and the operations have been depicted in a schematic diagram for ease of understanding. The intertwined functioning of organizational set-up is also discovered.

The paper will benefit entrepreneurs, mining and geology students, new recruits, and professionals in allied services linked to large iron ore mines. It offers valuable insights for knowledge enhancement, operational manual preparation and further research endeavours.

The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites.

The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure.

An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods.

The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.