Analysis of the causes of defects in ground floor systems of residential buildings

Gurmu, Argaw Tarekegn; Krezel, Adam; Mahmood, Muhammad Nateque

doi:10.1080/15623599.2020.1860636

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Structural integrity was modelled since the variable is significant in choosing builders. Previous studies conducted in Victoria, Australia, showed that cracks in the structure of the homes are the most common defects (Gurmu, Krezel and Mahmood, 2020). Hence, structural integrity was chosen for further analysis.…”

Section: Methodsmentioning

confidence: 99%

Modelling Customers’ Perception of the Quality of Services Provided by Builders: A Case of Victoria, Australia

Gurmu

Galluzzo²,

Kite³

2021

CEB

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Consumer satisfaction is essential for quality assurance, business survival and economic prosperity. It can also be used as an indicator of the occurrence of defects in the houses delivered by builders. The objective of this study is to compare the quality of services provided by volume and small builders, and to develop a model for predicting the chance of occurrence of structural defects in houses. A list of home builders was obtained from Australia’s Housing Industry Association media release 2019. Thereafter, customer reviews of 10 volume builders and 107 small builders were obtained from publicly available data. Overall, 2336 reviews for volume-builders and 2037 reviews for small builders were analysed quantitatively. Further, using the scores provided by customers, the probability-based regression model for the structural integrity of residential buildings was developed. Generally, the research found that for volume-builders, customers have the highest satisfaction level for ‘customer service’ and the lowest satisfaction level for ‘plumbing and waterproofing’ work. However, for small builders, customers have the highest confidence in the ‘structural integrity’ of their buildings and the least confidence in projects ‘timeliness’. Clients can use the stochastic-based model to predict the probability that a builder could deliver a house with low structural defects. The model showed that if a customer service score for a particular builder is less than 3.3, then there is a higher chance of having structural defects. This research contributes to the body of knowledge by developing and validating the logistic regression model that can be used as a tool to assess the quality of services provided by home builders. Moreover, the research provides useful information which can assist builders to improve the quality of services they provide.

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Section: Methodsmentioning

confidence: 99%

Modelling Customers’ Perception of the Quality of Services Provided by Builders: A Case of Victoria, Australia

Gurmu

Galluzzo²,

Kite³

2021

CEB

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“…Damage to the building will occur if there is an error in calculating the load or a failure in work. In general, the appearance of cracks in the walls indicates the presence of initial damage to the structure [9], while in certain situations, cracks appear on the floor of [10]. This fracture in the wall is the building's way of communicating to the homeowner that there is an imbalance in the load distribution.…”

Section: Structure In the Buildingmentioning

confidence: 99%

Diagnosis and Repair of the Cracking House Next to the River

Pramono

Mahendra

Wijaya

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Natural disasters and human error factors can cause damage to buildings. Human error causes various issues, including inadequate craftsmanship in the mixing of building materials and preliminary structural design and specification. Inadequate knowledge of the building’s structure and the type of soil on which it stands is also one of the fault designs in construction. This study utilises a case study methodology divided into three sections: soil analysis, building analysis, and techniques for avoiding and repairing the research object. A literature review is essential to ascertain the type of soil and the proper footing to utilise when examining the soil. In the building analysis, it is necessary to determine the damage of the building and its repair. These steps demanded were to prevent further damage by adding structures to reinforce existing structures in load distribution. After determining that the structure is safe from further damage, the final step is to repair the walls, floor, and ceiling. These improvements are part of an effort to ensure the building’s long term sustainability. The final phase in home restoration is to decorate the interior to make the residents feel comfortable.

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“…This research proposes a Deep Learning algorithm with a framework (H2O) that is already available in RapidMiner. RapidMiner is software for machine learning and data mining because it has flexible operators for data output and input in various file formats and contains more than 100 learning schemes for classification, regression, and clustering [8], [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

Al Habesyah,

Herteno,

Indriani

et al. 2024

j.electron.electromedical.eng.med.inform

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TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.

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Analysis of the causes of defects in ground floor systems of residential buildings

Cited by 9 publications

References 30 publications

Modelling Customers’ Perception of the Quality of Services Provided by Builders: A Case of Victoria, Australia

Modelling Customers’ Perception of the Quality of Services Provided by Builders: A Case of Victoria, Australia

Diagnosis and Repair of the Cracking House Next to the River

Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

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