Search results

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

Concrete structures undergo early and fast deterioration, which causes defects such as cracks, water leaks and delamination, resulting from a lack of or inefficient maintenance practices. To improve this behaviour, this paper aims to develop a maintenance strategy benchmarking model for concrete structures.

Fuzzy logic toolbox on MATLAB R2018a was used to develop the proposed model and it was applied to two cases. A comprehensive literature search was done to review common concrete defects, their impact on the performance and functionality of the structure, effectiveness of maintenance strategies and previous maintenance benchmarking models. The literature findings were further validated through expert interviews which have been incorporated in the model.

Case study results show that preventive maintenance (PM), predictive maintenance (PdM) and corrective maintenance (CM) strategies are required more or less in similar combinations for maintenance of concrete roof structures. The best combination for case 1 is 36.42% PM, 35.40% PdM and 28.18% CM, and for case 2 is 35.93% PM, 35.08% PdM and 28.99% CM. According to suitability, they can be ranked as PM > PdM > CM.

This model will contribute as a comprehensive decision-making tool for building/facility managers. The findings further carry a strong message to those who practice only CM in their buildings.

The improper management of conventional systems for recording, diagnosing and analysing information appears to be a major problem in the maintenance of heritage buildings. This is because of a lack of detection of patent or latent defects, ineffective repair methods and inappropriate planning decisions, which increase the risks of failure throughout the lifetime of heritage buildings. The paper aims to discuss this issue.

The concept of maintenance management practices, as well as techniques and tools from information and communication technology, was reviewed to identify suitable processes and construct an information database of maintenance and management techniques. A maintenance index framework was then formulated by analysing these concepts.

The initial findings identify the need for decision-making support in the diagnosis of heritage building maintenance based on robust data collection. This should include the selection of a durable replacement design and material or an appropriate rehabilitation method for managing quality and reducing associated defects.

Further research into the maintenance of heritage buildings should consider various aspects in terms of sustainability. The paper concludes that implementing a maintenance index framework can help improve the decision-making performance with regard to the design, construction and maintenance operations of heritage concrete buildings.

The purpose of this paper is mainly the quantitative and the qualitative analysis of a reinforced concrete slab containing two types of delaminations: voids and honeycomb, with different sizes and depths.

In this paper the paper adopts the infra-red thermography as a sounding method. It is used as a tool to estimate the change in temperature or thermal contrast induced by the presence of a defect in specimen besides using numerical simulation based on FEM to develop the prediction of temperature development in concrete structure.

This study shows that the numerical methods can be used to evaluate and validate the experimental results. The coupling of the simulation and the experimentation can be of a great utility because it allows predicting the results before beginning the experimentation.

The paper finds that the use of FEM in the prediction of temperature evolution in concrete, and the validation of the numerical simulation with the results obtained by the experimental measurements have a key importance in the study of civil engineering structure.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

The purpose of this paper is to discuss the system approaches and their application for managing IBS building in the context of Malaysian maintenance practice. Currently, the maintenance management method has affected the efficiency of the complex and high-rise industrialised building system (IBS) building maintenance management in Malaysia. Many issues such as poor service delivery, limited budgets, less competent staff and defect repetition emerged because of the usage of conventional method applications (paper-based form). The data revealed that the practice of maintenance management for complex and high-rise IBS buildings needs to be digitalised.

This qualitative research was carried out by conducting literature review and semi-structured interviews. Eight major maintenance organisations were selected based on a conventional method of practice in managing maintenance for complex and high-rise IBS buildings. The computerised system was developed using a data flow diagram and coding. Subsequently, the prototype system was tested.

By having this prototype system, the defect diagnosis and decision-making process become easier, faster and cost-effective in facilitating the maintenance assessment, defect diagnosis and control in relation to IBS building structure components.

In conclusion, the prototype system may improve the effectiveness of maintenance management practices for IBS building structure components in reducing defect design risks such as design calculation error to provide high-quality IBS building structure components for a safe and healthy environment.

The purpose of this paper is to focus on the development of an integrated computerised maintenance management system to improve the information storage of design and construction, diagnostic and defect risk assessments on IBS building through the integration of building information modelling (BIM).

The methodology used interviews with the IBS building client/maintenance contractor in Malaysia to gather information about maintenance management problems, approaches to address problems, information and communication technology implementation and use of emerging technologies, in addition to prototyping a system development life cycle for system development.

Relevant process flowchart documents of system development were obtained from the case study and reviewed to assist in providing an automation technique for decision-making and structural defect diagnostic operation through the integration of Visual Basic.Net, MS Access and Autodesk Revit software.

This research focuses on automatic bidirectional communications between an Expert System and BIM on a database level. Adoption of the approaches suggested in the research will enable the system to promote the development of zero IBS building maintenance.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

Concrete decay has become a major ongoing problem for the developed world, affecting all manner of structures. The purpose of the reported research is thus to advance the prospects for the realisation of high capacity, robotic repair systems through sensor technology. Here, the particular target is the removal of defective concrete by the hydro‐erosion method. The main advantages of the method are that it is kind to the structure while having the potential to produce high definition excavations. Sensing has been investigated for both prediction of the hydro‐erosion task and real‐time process feedback. The latter is complicated by the extremely destructive hydro‐erosion environment, which precludes the use of conventional sensing probes. For this, vibration and process noise have been investigated to determine if diagnostic characteristics are detectable. To support the task, a predictive basis has been developed using non‐destructive testing (NDT) sensors within a data fusion model. Covermeter, rebound hammer, impact echo and surface dampness NDT data are fed into this. Progress is reported on this part of the ongoing research.

This paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings.

In this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds.

The developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested.

Future research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization.

This study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.