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This paper introduces a novel multi-setup merging method and assesses its performance using simulated response data from a Finite Element (FE) model of a five-storey frame and experimental data from a cantilever beam tested in a laboratory setting.

In the research conducted at the Central Building Research Institute (CBRI) in Roorkee, India, a cantilever beam was examined in a laboratory setting. The study successfully extracted the modal properties of the multi-storey building using the merging technique. Identified frequencies and mode shapes provide valuable insights into the building's dynamic behavior, which is essential for structural analysis and assessment. The sensor layout and data merging approach allowed for the capture of relevant vibration modes despite the limited number of sensors, demonstrating the effectiveness of the methodology.

The results show that reducing the number of sensors can impact the accuracy of the mode shapes. It is recommended to use a minimum of 8 sensor locations (every two floors) for the building under study to obtain reliable benchmark results for further evaluation, periodic monitoring, and damage identification.

The results demonstrate that the developed algorithm can improve the system identification process and streamline data handling. Furthermore, the proposed method is successfully applied to analyze the modal properties of a multi-storey building.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

To mitigate the problems in sensor-based facility management (FM) such as lack of detailed visual information about a built facility and the maintenance of large scale sensor deployments, an integrated data source for the facility’s life cycle should be used. Building information modeling (BIM) provides a useful visual model and database that can be used as a repository for all data captured or made during the facility’s life cycle. It can be used for modeling the sensing-based system for data collection, serving as a source of all information for smart objects such as the sensors used for that purpose. Although few studies have been conducted in integrating BIM with sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between FMs and Internet of Things (IoT) companies in cases encountered failed sensors has received the least attention in the technical literature. Therefore, the purpose of this paper is to conceptualize and develop a BIM-based system architecture for fault detection and alert generation for malfunctioning FM sensors in smart IoT environments during the operational phase of a building to ensure minimal disruption to monitoring services.

This paper describes an attempt to examine the applicability of BIM for an efficient sensor failure management system in smart IoT environments during the operational phase of a building. For this purpose, a seven-story office building with four typical types of FM-related sensors with all associated parameters was modeled in a commercial BIM platform. An integrated workflow was developed in Dynamo, a visual programming tool, to integrate the associated sensors maintenance-related information to a cloud-based tool to provide a fast and efficient communication platform between the building facility manager and IoT companies for intelligent sensor management.

The information within BIM allows better and more effective decision-making for building facility managers. Integrating building and sensors information within BIM to a cloud-based system can facilitate better communication between the building facility manager and IoT company for an effective IoT system maintenance. Using a developed integrated workflow (including three specifically designed modules) in Dynamo, a visual programming tool, the system was able to automatically extract and send all essential information such as the type of failed sensors as well as their model and location to IoT companies in the event of sensor failure using a cloud database that is effective for the timely maintenance and replacement of sensors. The system developed in this study was implemented, and its capabilities were illustrated through a case study. The use of the developed system can help facility managers in taking timely actions in the event of any sensor failure and/or malfunction to ensure minimal disruption to monitoring services.

However, there are some limitations in this work which are as follows: while the present study demonstrates the feasibility of using BIM in the maintenance planning of monitoring systems in the building, the developed workflow can be expanded by integrating some type of sensors like an occupancy sensor to the developed workflow to automatically record and identify the number of occupants (visitors) to prioritize the maintenance work; and the developed workflow can be integrated with the sensors’ data and some machine learning techniques to automatically identify the sensors’ malfunction and update the BIM model accordingly.

Transferring the related information such as the room location, occupancy status, number of occupants, type and model of the sensor, sensor ID and required action from the BIM model to the cloud would be extremely helpful to the IoT companies to actually visualize workspaces in advance, and to plan for timely and effective decision-making without any physical inspection, and to support maintenance planning decisions, such as prioritizing maintenance works by considering different factors such as the importance of spaces and number of occupancies. The developed framework is also beneficial for preventive maintenance works. The system can be set up according to the maintenance and time-based expiration schedules, automatically sharing alerts with FMs and IoT maintenance contractors in advance about the IoT parts replacement. For effective predictive maintenance planning, machine learning techniques can be integrated into the developed workflow to efficiently predict the future condition of individual IoT components such as data loggers and sensors, etc. as well as MEP components.

Lack of detailed visual information about a built facility can be a reason behind the inefficient management of a facility. Detecting and repairing failed sensors at the earliest possible time is critical to ensure the functional continuity of the monitoring systems. On the other hand, the maintenance of large-scale sensor deployments becomes a significant challenge. Despite its importance, few studies have been conducted in integrating BIM with a sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between facility managers and IoT companies in cases encountered failed sensors. In this paper, a cloud-based BIM platform was developed for the maintenance and timely replacement of sensors which are critical to ensure minimal disruption to monitoring services in sensor-based FM.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

The purpose of this research is to focus on the evaluation of heritage buildings' sustainability. BIM modeling was necessary for the design of the sustainability assessment model for Heritage Buildings (SAHB). Using ArchiCAD®, energy simulations were performed for two case studies (Murabba Palace, Saudi Arabia, and Grey Nuns Building, Canada), and the developed model was validated through sensitivity analysis.

Heritage buildings (HBs) are unique and must be preserved for future generations. This article focuses on a sustainability assessment model and rating scale for heritage buildings in light of the need for their conservation. Regional variations were considered in the model development to identify critical attributes whose corresponding weights were then determined by fuzzy logic. Data was collected via questionnaires completed by Saudi Arabian and Canadian experts, and Fuzzy TOPSIS was also applied to eliminate the uncertainties present when human opinions are involved.

Results showed that regional variations were sufficiently addressed through the multi-level weight consideration in the proposed model. Comparing the nine identified factors that affect the sustainability of HBs, energy and indoor environmental quality were of equal weight in both case studies.

This study will be helpful for the design of a globally applicable sustainability assessment model for HBs. It will also enable decision-makers to prepare maintenance plans for HBs.

The study investigates the performance of a three-story unprotected steel moment-resisting frame (SMRF) designed for high seismic demand in the fire-only (FO) and post-earthquake uniform and traveling fires (PEF). The primary objective is to investigate the effects of seismic residual deformation on the structure's performance in horizontally traveling fires. The traveling fire methodology, unlike conventional fire models, considers a spatially varying temperature environment.

Multi-step finite element simulations were carried out on undamaged and damaged frames to provide insight into the effects of the earthquake-initiated fires on the local and global behavior of SMRF. The earthquake simulations were conducted using nonlinear time history analysis, whereas the structure in the fire was investigated by sequential thermal-structural analysis procedure in ABAQUS. The frame was subjected to a suite of seven ground motions. In total, four horizontal traveling fire sizes were considered along with the Eurocode (EC) parametric fire for a comparison. The deformation history, axial force and moment variation in the critical beams and columns of affected compartments in the fire heating and cooling regimes were examined. The global structural performance in terms of inter-story drifts in FO and PEF scenarios was investigated.

It was observed that the larger traveling fires (25 and 48%) are more detrimental to the case study frame than the uniform EC parametric fire. Besides, no appreciable difference was observed in time and modes of failure of the structure in FO and PEF scenarios within the study's parameters.

The present study considers improved traveling fire methodology as an alternate design fire for the first time for the PEF performance of SMRF. The analysis results add to the much needed database on structures' performance in a wide range of fire scenarios.

Heritage buildings are significant for their historical and architectural value. Due to the lack of rating systems designed specifically for heritage buildings, it is essential to develop and validate a heritage building assessment tool that considers its specific characteristics. The purpose of this study is to provide an extensive review of research on Sustainability of Heritage Buildings (SHBs).

This review highlights methodologies applied in SHBs research and analyzes major global rating systems in order to identify their deficiencies for SHBs assessment. A systematic review was employed and articles from the top 10 high impact factor journals were studied. Twelve major global rating systems and their assessment criteria were identified.

Significant variability was observed among the assessment tools since each tool assesses several criteria, factors and indicators that fit its local context. Part of this variability can also be seen in the rating scales, threshold values and accreditation titles. As a result, the final sustainability ranking for a given building cannot be compared among the 12 rating systems. Most importantly, these systems fail to analyze some factors such as energy that are considered important with respect to heritage building assessment.

Since no specific rating system could be identified in this review as the most appropriate for heritage buildings, a new sustainability assessment tool that is specific to heritage buildings should be developed. Such a tool will enable facility managers to evaluate and improve the sustainability of their heritage buildings while preserving them.

There are a number of benefits associated with two-way concrete flat slab construction for office buildings, parking garages and apartments - for example, reduced formwork, prompt erection, flexibility of partitions, and minimal increase in story heights. However, concrete flat slabs could be quite vulnerable to punching shear failure in the event of a fire. The objective of the present article is to provide a state of the art review of the existing research and the issues associated with concrete flat slabs in fire and elevated temperature. There are a number of experimental and analytical studies on the punching shear behavior of concrete flat slabs in ambient conditions, available in the literature. Based on these studies, it is found that punching shear capacity in ambient condition is affected by many factors, which may not remain constant during a fire exposure. Only a limited number of studies on concrete flat slabs for punching shear failure in fire are available. This paper reviews the available experimental and analytical studies, standards and codes to address the research gap in estimating of punching shear strength of concrete flat slab-column connections without shear reinforcement.