Importance of building maintenance at the time of architectural design of buildings rehabilitated with heritage value

During architectural conception phase,building maintenance problematic is mostly a result of the unintentional use of preconceived architectonical solutions rather than a consequence of a specific influence of maintenance requirements.Hardly the architect in the act of design understands the importance of these solutions in the service life span of a building.Being aware of this,is it possible for the architect to be supplied with a decision support system that allows him to consider the implications of building maintenance since the early design phases? Having awareness of this problem and its consequences in the early design phases a research project was started at the Faculty Engineering of the University of Oporto(FEUP),under which the implications of building maintenance in the act of architectural design is studied.This article presents the methodology developed to identify the needs of maintenance of buildings based on a DSS-decision support system that provides simple tools the architect can use in design phase.This methodology is based on decomposition of building parts-Elements Source of Maintenance ESM-,and subsequently,a set of functional requirements that determine the performance regarding building maintenance on account of architectural decisions.Relevant maintenance actions are defined: Inspection,Pro-action,Cleaning,Correction,Replacement,Legal enforcement,Limits of use.One can thus set up a relationship between the act of design and its performance framework based on behavior,intervention and the ownership of the work of architecture.Using a Multicriteria Analysis(MCA) a qualitative evaluation of different options based on maintenance requirements accomplishment.Conclusions on the importance of architectural conception concerning the building maintenance were clearly arrived at and the utility of the developed decision support tool was also highlighted.

Risk Analysis： A Methodology Applicable to the Building Inspection

The lack of care related to maintenance management is directly linked to the absence of plans for maintenance of buildings. Because of that, there is a big incidence of building accidents caused by the negligence of managers and the lack of investment. The research has shown that, although building inspection is a consolidated building maintenance tool, managers do not prepare their building maintenance plan, nor use building inspection as a tool, leading the buildings to the premature obsolescence. The research calls attention to the building inspection technique that can be used to evaluate building maintenance and conservation. It is also a tool for analysis and investment planning based on actions to solve failures and anomalies that might come out during the building inspection. Those failures and anomalies are classified according to their degree of risk, determining the technical priorities of the investment adjustment in the maintenance plan. The paper presents a risk analysis methodology which classifies inspect elements in a building, determining the priority order of services to be executed on a scale from minimum risk （1） to imminent risk （5）. The method shows a tool for building inspection, and it serves as a guideline for building maintenance interventions.

Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.

A whole-building data-driven fault detection and diagnosis approach for public buildings in hot climate regions

Fault detection and diagnosis(FDD)approaches comprise three main pillars:model-based,knowledge-based,and data-driven strategies.Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system’s background;yet,significant amounts of data is required,which often poses challenges to researchers.Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods,it has been used in data-driven FDD analysis.However,the majority of FDD approaches are implemented at the system level of buildings.However,most buildings have numerous systems with distinct features.Furthermore,using individualised system-level analysis makes it difficult to see system-to-system relationships.Currently,there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios,so as to identify a wider range of energy consumption related faults in buildings.Furthermore,since data-driven approaches significantly depend on the quantities of training data,it becomes challenging to diagnose faults that have limited features.As a result,this study diagnoses numerous building systems faults,including single and simultaneous faults with limited features.This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas,employing data-driven FDD methodologies.Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes.Furthermore,feature extraction methodologies were compared to quantify their potential for improving the diagnosis.In addition to the classification evaluation metrics,one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences.RF classifier obtained highest classification accuracy during validation and testing with about 90%,indicating a promising performance in whole-building faults analysis.The adoption of feature extraction techniques did not improve classification performance,thereby emphasising that some classifiers may perform better with high-dimensional datasets.