Masonry structures in historical sites are deteriorating due to ageing and man-made activities. Regular inspection and maintenance work is required to ensure the structural integrity of historic structures. The inspection work is typically carried out by visual inspection, which is costly and laborious, and yields to subjective results. In this study, an automatic image-based crack detection system for masonry structures is proposed to aid the inspection procedure. Previous crack detection systems generally involve the extraction of hand crafted features, which are classified by classification algorithms. Such approach relies heavily on feature vectors and may fail as some hidden features may not be extracted. In this study, we propose a crack detection system which combines deep Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). CNN is used in extracting features from RGB images and SVM is used as an alternative classifier to a softmax layer to enhance the classification ability. A dataset containing images of cracks from masonry structures was created using a digital camera and an unmanned aerial vehicle from historical sites. The images were used for training and validating the proposed system. It is shown that the combined CNN and SVM model performs better than the model using CNN alone with the detection accuracy of approximately 86% in the validation images. It is also shown that the system can be used to detect cracks automatically for the images of masonry structures, which is useful for inspection of heritage structures.
Purpose
The purpose of this paper is to measure the business value of IT (BVIT) and illustrate the relationship between IT practices and BVIT.
Design/methodology/approach
The paper uses a case study approach to collect the subject firm data over a period of one year. The data are about various IT systems used in the firm and their associated capital and operational cost components. The derived data are then compared with industry benchmarks.
Findings
The IT practices employed by the firm enable it to achieve a BVIT which is higher than the industry norm, from both strategic and operational perspectives.
Research limitations/implications
In this study, a year’s worth of data from a single firm is considered. The temporal frame of the research data limits the generalization of the results. To improve the generalizability, data from many years and across many firms may be used.
Practical implications
The paper provides insights to managers to identify the measures of BVIT. Further, managers can make necessary interventions based on IT practices to derive IT capabilities which, in turn, impact the firm’s performance.
Originality/value
The contribution of the work is manifold: illustration of the relationship between IT practices and BVIT; illustration of a methodology to evaluate firm-level BVIT; and an approach to collect IT expenses – both capital and operational level.
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