Deep learning smartphone application for real‐time detection of defects in buildings

Abstract

Condition assessment and health monitoring (CAHM) of built assets requires effective and continuous monitoring of any changes to the material and/or geometric properties of the assets in order to detect any early signs of defects or damage and act on time. Most of the traditional CAHM techniques, however, depend on manual labour despite that, in some cases, the inspection environment can be unsafe and could lead to low efficiency or misjudgement of the severity of the defect. In recent years, computer vision techniques have been proposed as an automated alternative to the traditional CAHM techniques as methods for extracting and analysing feature-related information from asset images and videos. Such methods have proven to be robust and effective solutions, complementary to current time-consuming and unreliable manual observational practices. This work is concerned with the development of a deep learning-based smartphone app, which allows real-time detection of four types of defects in buildings, namely, cracks, mould, stain and paint deterioration. Since smartphones are widely available and equipped with highresolution cameras, this application can offer a practical, low-cost solution for condition assessment procedures of built assets. The obtained results are promising and support the feasibility and effectiveness of the approach to identify and classify various types of building defects. K E Y W O R D Scondition assessment and health monitoring, deep learning, defects in buildings, object detection, smartphone applications | INTRODUCTIONDefects are defined as the deterioration of building features and services to unsatisfactory quality levels of the requirements of the users. 1 Defects and deterioration are common problems in built structures; they are a major concern of buildings and require significant attention. Although various defects are more common in old buildings, they can occur in new buildings as well. 2 Workers' faults, natural causes, poor maintenance and the age of the building are amongst the common causes of defects in built structures. 3 There are many types of building defects. In general, these can be divided into two categories: structural and non-structural defects. Structural defects are those which occur in a

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

“…Therefore, the initial model is trained with a considerable amount of data which can take a long time and later transferred to smaller devices, such as smartphones for damage detection, for example, cracks or concrete spalling in a fraction of a second, as demonstrated by Perez and Tah. 318 Other than the indirect and mobile approaches, smartphones with computer vision can be used for measuring displacement on different structural elements. Validation studies for evaluating mobile-SHM were carried out by Yu et al 319,320 The results show the suitability of smartphones for mini-SHM systems.…”

“…Therefore, the initial model is trained with a considerable amount of data which can take a long time and later transferred to smaller devices, such as smartphones for damage detection, for example, cracks or concrete spalling in a fraction of a second, as demonstrated by Perez and Tah. 318…”

Deep Learning-Based Defect Prediction for Mobile Applications

“…Perez and Tah developed a mobile application to detect defects on buildings using deep learning techniques. The application worked with a smartphone camera to recognize various problems on buildings [18]. Zhao et al (2022) proposed a model for imbalanced datasets of 15 Android applications.…”

Real‐time corrosion monitoring of an ultra‐high performance fibre‐reinforced concrete offshore raft by using an autonomous sensor system

“…19 The next challenge is to implement intelligent algorithm-based and deep learning-based damage identification methods in nextgeneration structural health monitoring systems. 20,21 The system employed in the present research work was INESSCOM (Integrated Network of Sensors for Smart Corrosion Monitoring). It is a smart system of embedded sensors that has been developed and patented by researchers from the Universitat Politècnica de València 22,23 to remotely monitor corrosion rate in multiple structure areas by applying an innovative measurement method.…”

“…The most recent works have focused on improving these sensor systems by incorporating wireless technology 18 because this not only facilitates their field installation but also represents considerable progress into incorporating reinforced concrete structures into the prevailing Internet of Things (IoT) paradigm 19 . The next challenge is to implement intelligent algorithm‐based and deep learning‐based damage identification methods in next‐generation structural health monitoring systems 20,21 …”