Building Defects Research Articles

Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications.

Understanding the Common Defects in Commercial Buildings: Types, Causes and Remedial Actions

Commercial building maintenance management constitutes a pertinent issue of global concern for all related stakeholders. In Malaysia, the management of commercial building maintenance plays a crucial role in achieving the goal of providing safety to occupants and the building itself. However, there is a paucity of studies that have comprehensively explored all dimensions of commercial building defects in relation to maintenance management. This research was also conducted to identify and analyze the typical defects that occur in commercial structures, understand their underlying causes, assess their impact on safety, functionality and propose effective remedial actions. The study utilized a qualitative approach for data collection. The findings indicated that cracked floors, floor tile failures, wall tiles failure, damaged ceiling, paint peeling and faulty light bulbs were some of the flaws that degrade commercial building. The study’s outcome revealed that defects not only deface the aesthetic appearance of the commercial buildings but also inhibit the functionality of the buildings and depreciate the overall satisfaction. The research limitations include using commercial buildings (shopping complexes or malls) that have been operating for more than 20 years. This study offers invaluable insights for maintenance organizations and maintenance department staff who are genuinely interested in improving commercial buildings’ maintenance management to optimize staff’s performance and enhance the user satisfaction of the buildings in Malaysia and globally.

Appraisal of the Aetiology and Pathology of Soil Settlement-Related Building Defects and Failures

Appraisal of the Aetiology and Pathology of Soil Settlement-Related Building Defects and Failures

Identifying and Categorizing Building Defects and Failures Caused by Overloading

Identifying and Categorizing Building Defects and Failures Caused by Overloading

Establishing the Taxonomy of Building Defects Triggered by Moisture Intrusion and Dampness

Establishing the Taxonomy of Building Defects Triggered by Moisture Intrusion and Dampness

Probabilistic Printability Maps for Laser Powder Bed Fusion Via Functional Calibration and Uncertainty Propagation

Abstract In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach toward inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to the literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar–Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack-of-fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.

Exploration of Timber Dry and Wet Rot Defects in Buildings: Types, Causes, Effects and Mitigation Methods

Exploration of Timber Dry and Wet Rot Defects in Buildings: Types, Causes, Effects and Mitigation Methods

Appraisal of Flat Roof Defects in Commercial Buildings and the Remediation Techniques

Appraisal of Flat Roof Defects in Commercial Buildings and the Remediation Techniques

Automatic detection and classification process for concrete defects in deteriorating buildings based on image data

ABSTRACT Defects in buildings accelerate the deterioration of building conditions and threaten the structural safety of buildings. Building condition assessments are essential to identify and assess building defects. However, with the vast number of buildings, individual inspections are nearly impossible, highlighting the need for innovative solutions. Therefore, this study presented a safety inspection process that automatically detects and classifies defects using image data that can be easily acquired at a low cost and confirms the location of defects through 3D modeling. A CNN model is developed to detect defects in images of aging buildings, and the proposed process is validated through a case study to demonstrate its effectiveness. The case study focused on the effectiveness and convenience improvement of the proposed process compared to the existing methods of inspecting the exterior of buildings. The effectiveness of the proposed process was reviewed through the case study, and it was confirmed that more efficient safety inspections could be supported using only image data compared to the existing methods. In particular, the safety inspection process of this study is expected to be applied in practical use within a short period since it is an easily utilizable method for inspectors.

Evaluating building defects and maintainability of tertiary institutions in Owerri Metropolis, Nigeria

Evaluating building defects and maintainability of tertiary institutions in Owerri Metropolis, Nigeria

Historical defects in buildings – No. 6: Regent Street disease

In this note, Hugh Docherty highlights the problem of corrosion of structural steelwork behind masonry elevations in buildings dating from the first half of the 1900s.

Lessons Learned from Information Sources on Building Defect Studies

Building defects are very prevalent and contribute significantly to a building’s economic value. There are numerous information sources on building defects that have significant potential for learning more about building defects. This study aimed to identify the information sources used in previous building defect studies and to identify the motivation for carrying out such studies. To fulfil this aim, a scoping study was carried out. The information sources identified included insurance companies, private databases, questionnaire surveys, lawsuits, building surveys, client complaint forms, and maintenance reports. This study found that insurance company and client complaint forms include the largest collection of real building defect cases, but such databases may lack detailed descriptions of the causes of the defects. The main purposes of the building defects studies identified here included design challenges, identification of defects, building maintenance management, quality management, systematization in data collection, providing an overview of typical defects, and classifying defects. Identification was found to be the most common purpose, indicating that the industry wants to learn more. This study identified research gaps in the climate perspective in relation to building defects. Most of the studies focused on the economical perspective, and none focused on the carbon footprint perspective. This study provides an overview of information sources, which can motivate stakeholders to collaborate in learning about the physical causes of building defects as well as about other key aspects such as fault, finances consequences, and procedural causes.

Application of the DEMATEL approach to analyse the root causes of building defects

Building defects are a common phenomenon in the construction industry. The negative consequences of building defects are not limited to waste generation or low resource efficiency. This challenge can have several direct and indirect implications such as cost overruns, unforeseen delays in project completion time, building occupants’ dissatisfaction and health and safety risks. Despite the increase in the number of studies investigating building defects in recent years, little is known about the prioritisation of their causes by considering the interaction among the causes of defects. Therefore, this study employed the decision-making trial and evaluation laboratory (DEMATEL) method to bridge this gap. Experts' opinion was sought using a questionnaire developed in the form of a matrix and semi-structured interview questions. Thereafter, the interactions among the causes of building defects were analysed and the most prominent causes are identified. The study showed that materials, workmanship and design are the major causes of building defects. Despite being specific context, the findings can be the basis for further research in this area with a focus on a range of different building typologies. Furthermore, the recommendations provided will act as a facilitator to minimise the occurrence of building defects. Lastly, the research findings can be considered in the planning stage of any construction project, to ensure the construction quality, reduce potential waste and enhance the circular economy and resource efficiency in the built environment sector.

Characterization of flexural fatigue behaviour of additively manufactured (PBF–LB) gyroid structures

Additive manufacturing (AM) holds remarkable potential for producing cellular materials with intricate structures and tailored mechanical properties. The study investigates the flexural fatigue behaviour of additively manufactured triply periodic minimal surface (TPMS) gyroid structures using laser powder bed fusion (PBF–LB) technique. The fatigue properties, especially the bending fatigue properties, of additively manufactured cellular structures are not well understood to date. The research aims to enhance understanding of bending fatigue in complex cellular geometries and assess the suitability of rotating bending tests. The PBF–LB process parameters were modified to study their impact on the specimen’s fatigue properties. The modified parameters led to increased surface roughness but significantly improved fatigue behaviour. This enhancement is attributed to a reduction in build defects, namely pores and finer grain size in thin-walled structures. The study also includes analysis of microstructure, hardness, surface roughness, and porosity of the specimens. The results indicate that optimizing process parameters for thin walled cellular structures can lead to substantial improvements in fatigue strength, at the expense of increased surface roughness. This finding offers practical insights for applications in which a rough surface finish may not be critical or even intentionally desired by the application. The research contributes to the understanding of additive manufacturing, cellular structures, and material testing, with potential implications for materials science and engineering applications.

Historical defects in buildings – No. 5: Wood-wool formwork

In this latest note, Hugh Docherty discusses the use of wood-wool slabs as permanent formwork to reinforced concrete slabs and the potential problems that can arise in this form of construction.

Detecting surface defects of heritage buildings based on deep learning

Abstract The present study examined the usage of deep convolutional neural networks (DCNNs) for the classification, segmentation, and detection of the images of surface defects in heritage buildings. A survey was conducted on the building surface defects in Gulang Island (a UNESCO World Cultural Heritage Site), which were subsequently classified into six categories according to relevant standards. A Swin Transformer- and YOLOv5-based model was built for the automated detection of surface defects. Experimental results suggested that the proposed model was 99.2% accurate at classifying plant penetration and achieved a mean intersection-over-union (mIoU) of over 92% in relation to moss, cracking, alkalization, staining, and deterioration, outperforming CNN-based semantic segmentation networks such as FCN, PSPNet, and DeepLabv3plus. The Swin Transformer-based approach for the segmentation of building surface defect images achieved the highest accuracy regardless of the evaluation metric (with an mIoU of 90.96% and an mAcc of 95.78%), when contrasted to mainstream DCNNs such as SegFormer, PSPNet, and DANet.

Historical defects in buildings – No. 4: Carbonation of concrete cover

Hugh Docherty examines the potential damage caused by carbonation in reinforced concrete, and offers brief advice on identification and remediation.

Factors influencing defects in residential buildings

Factors influencing defects in residential buildings

Historical defects in buildings – No. 3: Alkali–silica reaction in concrete

Hugh Docherty continues this series by examining the potential damage caused by alkalis and silica reacting in concrete, and offering brief advice on identification and remediation.

Development of a Prediction System for Design Defects of Buildings

The research aimed to establish a quantitative model to predict the occurrence of five types of design defects: unsafe design, incompatible design, inefficient design, design ambiguity, and design delays. It also examined the contractor's liability for design to determine who is ultimately responsible for design defects disputes between the employer and contractor. The study employed a descriptive methodology to identify fifteen potential influencing factors on design defects. Data were collected from 42 construction projects and tested using Design of Experiments (DOE). Out of these fifteen factors, only five—design schedule, design firm experience, stakeholders’ involvement, project delivery system, and information accuracy—had a significant impact on design defects. These factors were used in the development of a new model to predict the occurrence of design defects. The model was validated using data that was held separate from the model development process for validation purposes. The calculated Mean Absolute Percentage of Error (MAPE) for the new model is 19%, which is considered a "good" prediction accuracy. The research achieved three significant milestones: 1) It identified the ultimate responsibility for the consequences of design defects in construction projects across different jurisdictions. This information can be used to resolve potential disputes between employers, designers, and contractors’ organizations. 2) It provided guidelines on how to minimize design defects in construction projects, thereby mitigating the consequences of such defects. 3) It developed a new model to predict the occurrence of design defects in construction projects. This model aids decision-makers in responsible organizations during the process of predicting the appropriate reserve to be added to the project schedule and budget. Consequently, it helps mitigate the risk of design defect-related delays and cost overruns. Doi: 10.28991/CEJ-SP2023-09-017 Full Text: PDF