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In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

With the trend of sustainable development growing worldwide, both the numbers of new mega building construction projects and renovations to existing high-rise buildings are increasing. At such construction sites, most construction workers can be described as performing various activities in indoor spaces. The literature shows that the indoor safety protection measures in such construction sites are often imperfect, resulting in an endless stream of incidents such as falls. Thus, this research aims at developing a flexible indoor safety warning system, based on Wi-Fi-generated channel state information (CSI), for monitoring the construction workers approaching restricted areas or floor openings. In the proposed approach, construction workers do not have to carry any sensors, and each indoor space only needs to have the specified Wi-Fi devices installed. Since deep learning methods are employed to analyze the CSI data collected, the total deployment time, including setting up the Wi-Fi devices and performing data collection and training work, has been measured. Efficiency and effectiveness of the developed system, along with further developments, have been evaluated and discussed by 12 construction safety experts. It is expected that the proposed approach can be enhanced to accommodate other types of safety hazards and be implemented in all mega building construction projects so that the construction workers can have safer working environments. [ABSTRACT FROM AUTHOR]

Critical infrastructure represents important national assets for producing or distributing continuous flows of essential goods or services. When one aspect of the critical infrastructure shuts down due to an external disruption, other portions of the system that need those goods or services are likely to stop shortly thereafter, thus exacerbating the damage caused by the external disruption. Literature on critical infrastructure interdependency (CII) shows a need for analyzing failure records with time information from different types of critical infrastructure. This paper presents a knowledge discovery process for CII to extract records of frequent patterns of critical infrastructure failure that are directly or indirectly triggered by external disruptions. The knowledge discovery process, including integration of critical infrastructure failure records and their transformation into the data format needed by a data mining algorithm, is described. The paper includes a discussion on a disaster mitigation approach that could be used to stop CII-related failure events, and it includes the analysis of the results of sample critical infrastructure failure records. Disaster mitigation officials can employ the proposed approach to explore CII and to design countermeasures when a disaster hits. DOI: 10.1061/(ASCE)CP.1943-5487.0000059 CE Database subject headings: Infrastructure; Databases; Algorithms; Data analysis; Decision making; Security; Hazards. Author keywords: Infrastructure; Databases; Algorithms; Data analysis; Decision making; Security; Hazards.

The adjustment of utilities prior to highway construction is a particularly complex and challenging operation. One strategy that has been adopted in the last two decades is to incorporate utility adjustment work into highway construction contracts. In theory, this approach eliminates or reduces some of the complications and risks of utility adjustments. This paper aims to identify and analyze the decision drivers for selecting this strategy and help alleviate its implementation challenges. To this end, a comprehensive literature review and several interviews with experts from state departments of transportation and utility companies were conducted. Assessment surveys regarding the performance of recent projects that used the combined strategy were performed. Conclusions and recommendations for future research are presented. DOI: 10.1061/(ASCE)0733-9364(2009)135:9(812) CE Database subject headings: Utilities; Highway and roads; Decision making; Construction management; Contracts.

As our society ages, more and more elderly or disabled people live in long-term care (LTC) facilities, which are vulnerable to fires and may result in heavy casualties. Because of the low mobility of LTC residents, firefighters often need to enter the facility to save people. In addition, due to LTC facility management needs, many doors or windows on the passages for a fire rescue operation may be blocked. Thus, firefighters have to employ forcible entry tools such as disk cutters for passing through, which may lengthen the rescue time if an incorrect route or tool is utilized. As new information technologies such as ontology and building information modeling (BIM) have matured, this research aims at proposing a BIM-based ontology model to help firefighters determine better rescue routes instead of using rules of thumb. Factors such as the path length, building components and materials encountered, and forcible entry tools carried are considered in the model. Real LTC fire investigation reports are used for the comparisons between the original routes and the ones generated by the proposed model, and seven experts joined the evaluation workshop to provide further insights. The experts agreed that using the proposed approach can lead to better fire rescue route planning. The proposed BIM-based ontology model could be extended to accommodate additional needs for hospital fire scenes, in the hopes of enhancing the efficiency and effectiveness of firefighters' rescue operations in such important facilities. [ABSTRACT FROM AUTHOR]

Highway projects in metropolitan areas often require that adjacent utilities be relocated to accommodate new or expanded highway facilities. Traditionally, utility owners have been responsible for their own relocations prior to highway construction. However, utility relocation delay was reported as one of the root causes of overall project delays in highway construction. In the USA, one strategic approach sporadically taken over the past 15 years by some state departments of transportation (DOTs) has been to combine utility relocation work with the highway contractor's scope of work. While the combined strategy offers many benefits, it does have its own set of challenges and disadvantages. Many of these difficulties involve disagreements between DOTs and utility owners over the appropriateness of the combined strategy for particular types of relocations. Hence, a negotiation-based decision support model (DSM) for determining whether to use the combined strategy was developed to provide guidance to both DOTs and utility decision makers during the planning and design phases of highways. A literature review and interviews with experts from both DOTs and the utility industry were conducted to identify significant factors as well as their impact levels on the decision. A DSM aiming at synthesizing the factors and orchestrating the negotiation process for the decision was proposed and verified. As suggested by the model evaluators, the proposed DSM can improve the quality of this complex decision-making process, especially for less experienced project managers or designers. [ABSTRACT FROM AUTHOR]

Transit-oriented development (TOD) is a crucial method for sustainable urban development and encouraging the use of public transit, especially for large densely populated cities such as Can Tho City, one of the top five cities in Vietnam. This study is aimed at identifying the factors which have a significant effect on the citizens' engagement of public transport in the TOD environment. Based on the literature review and expert knowledge, 3 hypotheses related to citizens' perceptions and citizen's engagement are developed. A questionnaire was accordingly established using structural equation modeling (SEM). As a result, a total of 8 factors with 31 stems were obtained. The survey questionnaires were distributed to citizens of Can Tho City with 749 responses collected for KMO test Chi-square of 0.905. The analysis shows that all three hypotheses stand: (1) the citizens' perception of local government's capacity plays an important role in the citizens' willingness to engage on public transport; the citizens' perception of the usefulness of (2) TOD and (3) public transport has a positive influence on the engagement on public transport system. The major contribution of this study is that it can help other cities in developing countries to apply TOD. [ABSTRACT FROM AUTHOR]