Search results

This project examines digital modeling strategies for existing buildings. In this context, it aims to question assumptions about the need for geometric accuracy and the efficacy of predefined ontologies. As a counterpoint to prevailing digital modeling strategies, this project proposes a digital modeling approach using a project-specific, emergent ontology.

Nishiki Market, in Kyoto, Japan, is studied as a test case. The emergent-ontology modeling process is introduced with an initial minimal set of operations including basic fold and trim operations applicable to surfaces. As the model develops iteratively, new situations are encountered for which existing rules are insufficient. In response, the model maker’s subjective judgment is invoked to introduce new operations, and ontological rules are allowed to expand.

The emergent-ontology approach, when executed on the Nishiki Market test case, enables representation of specific architectural qualities, highlighting semantic distinctions between digitally modeled elements of real-world features. The modeling approach generated project-specific knowledge, informing disciplinary understanding. Ontological emergence enabled semantic relationships to be disclosed and newly constructed.

The project proposes a novel methodology using an emergent ontology for digitally modeling existing buildings. Instead of remaining within the limitations a predefined ontology, the model maker’s subjective decisions shape the model’s ongoing development. This interpretive approach allows project-specific knowledge generation while challenging prevailing assumptions about accuracy and consistency in digital models of existing buildings.

Due to the influence of complex and intersecting factors, self-governed public open spaces (POSs) (managed by local communities) are subject to collective action dilemmas such as tragedy of the commons (overexploitation), free-riding, underinvestment and mismanagement. This review paper adopts a multi-dimensional and multi-tier social-ecological system (SES) framework proposed by McGinnis and Ostrom, drawing on collective action theory to explore the key institutional-social-ecological factors that impact POS self-governance.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was utilized to systematically screen and review the relevant literature for the period from 2000 to 2023 in three databases: Web of Science, Scopus and Google Scholar. A total of 57 papers were chosen for in-depth analysis.

The literature review identified and categorized several variables associated with the self-organizing system of POS; consequently, an SES-based POS management framework was developed for the first time, consisting of 114 institutional-social-ecological sub-variables from different dimensions and three levels. Compared to ecological factors, among others, governance organizations, property-rights systems, socioeconomic attributes and actors' knowledge of SES have been commonly and primarily studied.

There is still room for the refinement of the conceptual SES-based POS collective action framework over the time (by adding in new factors), and indefinitely empirical research validating those identified factors is also worth to be undertaken, particularly testing how SES factors and interaction variables affect the POS quality (collective action).

The findings of this study can provide local policy insights and POS management strategies based on the identification of specific SES factors for relevant managers. Moreover, this research makes significant theoretical contributions to the integration of the SES framework and collective action theory with POS governance studies.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

This study establishes an ontology-based framework for rework risk identification (RRI) by integrating heterogeneous data from the information flow of the prefabricated construction (PC) process. The main objective is to enhance the automation level of rework management and reduce the degree of reliance on human factors and manual operations.

The proposed framework comprises four levels aimed at managing dispersed rework risk knowledge and integrating heterogeneous data. The functionalities were realised through an integrated ontology that aligned the rework risk ontology with the PC ontology. The ontologies were developed and edited with Protégé. Ultimately, the potential benefit of the framework was validated through a case study and an expert questionnaire survey.

The framework is proven to effectively manage rework risk knowledge and can identify risk objects, clarify risk factors, determine risk events, and retrieve risk measures, thereby enabling the pre-identification of prefabricated rework risk (PRR) and improving the automation level. This study is meaningful and lays the foundation for the application of other computer methods in rework management research and practice in the future.

This research provides insights into the application of ontology to solve rework risk issues in the PC process and introduces a novel risk management method for future prefabricated project research and practice. The findings have significant theoretical value in terms of enriching the methods of risk assessment and control and the information management system of prefabricated projects.

The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

By inspecting the special connection between negligence and the causes of negligence, this study evaluates the reasonableness of negligence offenses elements from the perspectives of “modes” and “rules.” This study considers that in the core concept of negligent manslaughter in the Criminal Code of Taiwan, “business” comprises an outstretched legal element; thus, “application by analogy” or “customary laws” should be prohibited as a legal basis or when applying the criminal code because those are not allowed under nulla poena sine lege. Nulla poena sine lege must be respected to release construction professionals from material risks in their judicial rights and interests.

This study used data mining analysis with a database of 204 cases where construction professionals were involved in an accusation of gross negligence manslaughter (GNM) (N = 486) between 1995 and 2021 to explore the reasons and distribution of these cases in the construction industry in Taiwan.

The results showed that the main reasons behind lawful GNM accusations against construction professionals are as follows: (1) the violation of employers' duty of care to prevent hazards caused in workplaces where falling and collapsing are concerns during construction, thus resulting in death; (2) gross negligence during design, construction and supervision, causing damages after natural disasters such as earthquakes and typhoons.

This study discusses the whole life circle of construction, starting from planning, design, construction and completion. However, the involvement of other offenses such as providing false statements, forgery, embezzlement, unjust enrichment and fraudulent tax evasion or criminal responsibilities stipulated in the Building Act or administrative punishments are beyond the scope of this study. Future studies will focus on foreign “business GNM” cases from judicial precedents with similar backgrounds to Taiwan in the construction industry to verify whether similar conclusions can be drawn and to examine their differences.

This study applied data mining and data analysis to the data and explored potential causality and patterns of GNM cases in judicial cases. The results of the analyses can be used as evidence for potential causality and thus facilitate construction professionals' self-reflection and contribute to the sustainable development of working environments for construction.

This study agrees with the removal of GNM titled “business” in the Criminal Code of Taiwan to achieve Sustainable Development Goals (SDGs) in the building industry. By doing so, national judicial and management systems will be in line with international standards, ensuring that everyone has equal access to justice.

Goal 16 of the SDGs by the United Nations aims to promote judicial equality, peace, justice and strong institutions. With this basis, this study collected and analyzed data in the field of criminal law and applied the theory of criminal offenses committed by negligence to real construction-related cases. This study especially discusses whether construction professionals were imposed with excessive responsibilities when a court enforced the “duty of care” that asked the professionals to bear the responsibility of results for events that should be and could be foreseen.

To overcome the limitations of traditional multi-attribute decision making (MADM) methods, which only provide deterministic rankings of decision objects, this paper proposes a novel multi-attribute 3WD model. This model presents three-parameter interval grey number decision-theoretic rough sets (TPIGNDTRSs), aiming to offer a reasoned interpretation of loss functions in grey environments and ensure objective assessment of conditional probabilities.

Firstly, the traditional equivalence relation is replaced with the probabilistic dominance relation (PDR), categorizing decision objects into two state sets in DTRS for more objective conditional probabilities. Secondly, as the three-parameter interval grey number (TPIGN) introduces the most probable value on the basis of the traditional two-parameter interval grey number, it provides a more comprehensive method for describing grey information. Consequently, integrating TPIGN into DTRS refines the interpretations of loss functions in grey environments. Finally, by utilizing two main sorting techniques, relative kernel and degree of accuracy ranking and possibility ranking, two types of 3WD rules with TPIGNDTRSs, are constructed.

This study has successfully developed and validated a new multi-attribute 3WD model. The model was tested in two distinct domains: evaluating innovation efficiency in high-tech enterprises and recommending movies in a practical case. The findings reveal that the model can effectively integrate relevant information of high-tech enterprises, provide the government with enterprise-level assessments, and gather consumer preferences to recommend the most suitable movies.

This study treats the loss function as grey information in the 3WD model but overlooks the grey nature of evaluation values, limiting its applicability. Additionally, the model’s reliance on subjective expert judgments and historical data to establish the loss function may affect its objectivity. The implications of this research are that the novel model overcomes traditional MADM limitations, enhancing decision-making quality and efficiency in complex and grey scenarios. The model’s successful application in evaluating high-tech enterprises and recommending movies illustrates its dual value in both theory and practice.

Initially, the model proposed in this study is of significant importance for the development of the 3WD field, as it successfully addresses the challenges of uncertain loss functions and unknown conditional probabilities in grey information environments. Moreover, by integrating the 3WD model with MADM problems, it has broken through the bottlenecks of traditional MADM methods, offering new perspectives and strategies for solving MADM issues. Therefore, this research not only advances theoretical research but also provides powerful tools for practical applications.

Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

This article aims to understand users' information needs in the metaverse communities and to analyze the similarities and differences between their information needs and those of users in Internet communities.

This study conducted semi-structured interviews with users in the metaverse communities to gather raw data. Grounded theory research methods were employed to code and analyze the collected interview data, resulting in the extraction of 40 initial concepts, 15 subcategories and 5 main categories. Based on Maslow’s hierarchy of needs theory, this paper constructs the hierarchical model of users' information needs in the metaverse communities. It compares the differences between users' information needs in the metaverse and Internet fields.

The user’s information needs in the metaverse communities are divided into two types: deficiency needs and growth needs. Deficiency needs have two levels. The first level is the demand for basic information resources. The second level is the users demand for information assistance. Growth needs have three levels. The first level is the need for information interactions. The second level is the need for community rules. The ownership information in the community rules can provide proof of user status, assets and so on. The third level is the need for users to contribute and share their own created information content.

This article presents the latest research data from in-depth interviews with users in the metaverse communities. It aims to help builders and managers of metaverse communities understand users' information needs and improve the design of virtual communities.

This study aims to explore network typology and the role of networks in immigrant entrepreneurs’ (IEs) business operations in South Africa (SA).

In-depth interviews were carried out with 25 IEs operating their business ventures in SA, selected via the purposive sampling method.

The findings indicated that IEs in SA use social networks most often, with minimal use of international business networks. In addition, the findings showed that IE networks’ key roles include providing them with access to referrals and resource provision. However, whilst resource provision emerged as a significant role, finance appeared as the main resource provided by networks.

To the best of the authors’ knowledge, this original paper provided theoretical and empirical contributions to research on network typology and its role for IEs. Thus, the study extended the current literature on the intersection of IEs and their networking behaviours.