Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

Risk Cost Measurement of Value for Money Evaluation Based on Case-Based Reasoning and Ontology: A Case Study of the Urban Rail Transit Public-Private Partnership Projects in China

Risk is demonstrated as one of the most crucial drivers of value for money (VFM) in public–private partnerships (PPP), but in previous studies, the risk cost estimation of the quantitative evaluation of VFM was still a dilemma that strongly depended on specialist discretion or had low methodological operability. This paper establishes a prediction model for estimating the risk cost in the phase of VFM evaluation through a combination of case-based reasoning (CBR) and ontology technology. PPP information ontology was established to provide the technical basis of knowledge representation for the CBR cycle. Then, according to whether the information data were quantitative or qualitative, similarity calculation methods were used for the retrieval of similar cases. The conceptual semantic similarity algorithm based on the ontology tree structure was well implemented to compare abstract information. After the most similar cases were extracted, a revision mechanism was followed when there were deviations in the similar cases. Finally, the risk costs of the target case were obtained by weighting the extracted similar cases based on the similarity. An empirical analysis was performed with 18 historical projects from the China Public–Private Partnerships Center. The results showed that the relative errors between the estimated and actual costs of total risk and retained risk were 11.05% and 2.41%, respectively. This indicates that the estimation model could achieve a better risk cost prediction with small errors, which validates the availability of the model. Based on the proposed model, this research establishes an extensible PPP information ontology model. It promotes the integration and interoperability of information knowledge in the PPP domain, which can be further expanded according to the requirements. Coherent accuracy is provided by the whole CBR-based measurement process, which has offered a systematic and objective method for the risk costs measurement of PPP projects.

Evolution of Electronic Procurement: Contemporary Review of Adoption and Implementation Strategies

Since the advent of the first form of electronic procurement (EP), the procurement process (i.e., the stages and underlying activities) has evolved to transform the world of commerce significantly. In the construction industry, EP has revolutionized the operational schema of traditional procurement and has paved the way for the development, advancement, and application of more intelligent tools for handling and executing procurement activities of infrastructure-related works. Although the development or adoption of EP-related tools has not been encouraging due to the industry’s conservative nature, in this study, we review the evolution and developments of EP to date. It explores the adoption and implementation strategies of extant and projected EP developments and develops a conceptual framework to explicate the developmental transitions of EP. While there are many studies on EP, there is still a long way to transition into a more intelligent execution of EP-related activities. A total of 64 EP-related manuscripts and other valuable studies were examined to realize the stipulated objective as well as identify the thematic constructs developed under the subject matter. The explorations conducted in this study are intended to contribute to the understanding of the extant discussion on both EP and smart procurement (SP), their adoption strategies, implementation, and applications in infrastructure projects. It elucidates the significant constructs that define and underpin the theoretical implications of EP and SP adoption and implementation. Lastly, the framework highlights the projected developments and transition phases of EP to intelligent procurement (IP).

Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies

Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new "open" information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.

Construction safety: an analysis of the cross-influence of economic, construction, and accident death factors

To explore the relations between the number of accident deaths (NCD) and various factors in construction safety, a multiple linear regression model was established, based on the panel data of economy, construction labor, and the number of accident deaths in 31 provinces (regions) of China from 2009 to 2018. The results show that the average total profits and taxes (TPAT), and the death rate of ten thousand people (DR) are positively correlated with NCD, while the labor productivity (LP) is negatively correlated with it. Under the condition that other factors remain unchanged, NCD increases by 0.02 per a hundred million CNY. However, if DR increases by one ten thousand, NCD will increase by 8.66 on average. On the contrary, when LP increases by a hundred million CNY per ten thousand people, NCD decreases by 0.19. The model also predicts NCD in different provinces and cities from 2019 to 2020 with the method of quadratic exponential smoothing. As well, practice results show that the model is effective and practical.

A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project

Early decision-making and the prevention of construction safety risks are very important for the safety, quality, and cost of construction projects. In the field of construction safety risk management, in the face of a loose, chaotic, and huge information environments, how to design an efficient construction safety risk management decision support method has long been the focus of academic research. An effective approach to safety management is to structuralize safety risk knowledge, then identify and reuse it, and establish a scientific and systematic construction safety risk management decision system. Based on ontology and improved case-based reasoning (CBR) methods, this paper proposes a decision-making approach for construction safety risk management in which the reasoning process is improved by integrating a similarity algorithm and correlation algorithm. Compared to the traditional CBR approach in which only the similarity of information is considered, this method can avoid missing important correlated information by making inferences from multiple sources of information. Finally, the method is applied to the safety risks of subway construction for verification to show that the method is effective and easy to implement.