Development of a Cloud-Based Real-Time Building Health Monitoring and Prediction System Using AI and IoT Sensors

In this study, a cloud-based real-time building health monitoring and prediction system using AI and IoT sensors was developed. To predict the building condition, which constitutes time-series data, statistical-based ARIMA and AI-based LSTM prediction models were designed, and the effectiveness of the proposed prediction models was experimentally verified using a 1/8-scaled miniaturized structure. The prediction accuracy in terms of MAPE (less than 1%) was experimentally confirmed to be satisfactory. Moreover, a method for analyzing dimensional structure deformation was developed by combining multiple sensor measurements, and its effectiveness was verified through the case study of a real earthquake-damaged building.

Assessment of Residential Buildings Condition In Government Built Housing Estates of Port Harcourt Municipality, Nigeria

A major key indicator for assessing the quality of an urban residential neighborhood is the building condition. A good building condition adds value to a neighborhood in terms of aesthetics, safety, security and comfort. The Rivers State Government has in 1986 - 1998 built 12 residential estates for her staff members in the study area. The responsibility of maintenance of the buildings has been left to the owner occupiers of those houses. What is the state of those houses which has been built for over 30years? It is on this note that this study is poised to ascertain the physical condition of buildings within the public housing estates in Port Harcourt municipality. This study is a quantitative research that belongs to the class called “descriptive research design”. Simple random sampling technique was used to select 108 respondents (household heads) from the six selected housing estates. Questionnaire, physical observation and digital camera were the tools used for data collection. Analysis of findings was presented descriptively in tables, charts and percentages. Among the twelve public housing estates identified within the study area, the simple random sampling method was used to select and study six of the estates which are: 1. Aggrey Housing Estate, 2. Marine Base Govt. Housing Estate, 3. Abuloma housing estate phase, 4. Ndoki Housing Estate, 5. Elekahia housing estate and, 6. Khana Street Housing Estate. The research findings show the physical condition of buildings within the public housing estates, 81.5% of the buildings has good foundation, 92.6% of the windows are in good condition, 95.4% of the buildings have water system. 65% of the respondents considered the overall housing condition of the estates as good (needs no repair) while 35% saw the housing condition as fairly good (needs minor repair).

An integrated space-based building maintenance management model using multi-objective optimization

PurposeThe purpose of this study is to minimize cost and minimize building condition. Weibull distribution approach was employed to generate deterioration curves over time. The third floor of Concordia University’s Engineering And Visual Arts (EV) Complex in Montreal, Canada, served as a case study to test the maintenance model and determine the optimal maintenance activities to be performed.Design/methodology/approachThis research has demonstrated that there is insufficient fund allocation for the maintenance of non-residential buildings. Therefore, this research focused on designing and developing a maintenance optimization model that provides the type of spaces (architectural system) in a building. Sensitivity analysis was used to calculate weights to validate the model. Particle swarm optimization, based explicitly on multiple objectives, was applied for the optimization problem using MATLAB.FindingsFollowing 100 iterations, 13 non-dominant solutions were generated. Not only was the overall maintenance cost minimized, but the condition of the building was also maximized. Moreover, the condition prediction model demonstrated that the window system type has the most rapid deterioration in educational buildings.Originality/valueThe model is flexible and can be modified by facility managers to align with the required codes or standards.

INTEGRATING MULTIBAND PHOTOGRAMMETRY, SCANNING, AND GPR FOR BUILT HERITAGE SURVEYS: THE FAÇADES OF CASTELLO DEL VALENTINO

The conservation of built heritage is a complex process that necessitates co-operative efforts. Holistic, integrated documentation constitutes a crucial step towards conservation by contributing to diagnosis and by extension to the effective decision-making about the required preventive and restorative interventions. It involves the recording of interdisciplinary data to produce objective diagnostical conclusions concerning the state of preservation. Although the developments in close-range sensing techniques allow increasingly accurate and rich data recording for heritage building condition surveys, the problem of combining them (to allow integrated processing) often remains unsolved. This is particularly true when surveys include vastly heterogenous documentation data. This work aims to discuss methodologies and implications of such integrations through a monumental heritage survey case – the Castello del Valentino in Turin (Italy). Visible-spectrum and infrared imagery is combined with photogrammetric techniques, terrestrial LiDAR, and microwave measurements conducted on the historical façades’ surfaces, to examine the comprehensiveness of the data fusion results, as well as conclusions that can be drawn regarding previous interventions and the current condition of the monument.

Detection of Building Defects Using CNN

Detection of defects together with cracks and spalls on wall surface in high-rise buildings may be a crucial task of buildings’ maintenance. purchasers area unit progressively searching for quick and effective suggests that to quickly and often survey and communicate the condition of their buildings in order that essential repairs and maintenance work will be tired a proactive and timely manner before it becomes too dangerous and big-ticket. If left unseen and untreated, these defects will considerably have an effect on the structural integrity and also the aesthetic side of buildings, timely and efficient strategies of building condition survey area unit of active want for the building house owners and maintenance agencies to switch the time- and labour-consuming approach of manual survey. so mistreatment the applying of deep learning technique of convolutional neural networks (CNN) in automating the condition assessment of buildings. the main target is to automatic detection and localisation of key defects arising from damp, patches, stains, cracks in buildings from pictures.