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The paper seeks to identify the determinants of business failure in construction and to predict the failure likelihood of construction companies by assessing their current situation based on both company‐specific and external factors.

A conceptual model is designed based on an extensive literature survey. The analytical network process together with the Delphi method is utilised to compute the importance weights of variables on business failure through interviews and discussions with experts. The applicability of the proposed model is tested on five companies to estimate their failure likelihood by using the findings derived from the analysis.

The results suggest the importance of organisational and managerial factors, including the efficiency of the value chain at the corporate level, the appropriateness of organisational decisions, and the availability of intangible resources for the survival of construction companies.

The findings of the analysis are limited to the experiences of three professionals in the Turkish construction industry. The performance of the model is only tested in five companies. The accuracy of the model may be improved by using the diverse experiences of a larger group of experts.

The proposed tool may act as an early warning system for construction companies by estimating the level of their failure likelihood. Companies may benefit from the findings of the model to assess their current situations and take necessary action to avoid possible business failures.

The knowledge and experiences of experts are used to obtain a complete model that accommodates both external and company‐specific variables, and more importantly the inter‐relations among them. Similar models may also be developed for companies in other industries to diagnose their bankruptcy or failure likelihood.

The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.

A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.

The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.

The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.

A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

Describes the ability of modern computer‐driven multivariate statistical analysis to deal with complex data and the development of statistical models for predicting financial distress. Applies multivariate techniques to 1986‐1991 financial ratio data for Australian failed (29) and nonfailed (42) companies; and explains the techniques used (principal components analysis, factor analysis, discriminant analysis and cluster analysis) and the different types of information they can provide to help identify the distress levels of companies. Predicts that multivariate methods will change the way researchers think about problems and design their research. An unusually clear exposition of the application of multivariate methods.

The purpose is to explore the differences and similarities between fraudulent financial reporting detection and business failure prediction (BFP) models, especially in terms of which explanatory variables and methodologies are most effective.

In total, 52 financial variables were identified from previous studies as potentially significant. A number of Taiwanese firms experienced financial distress or were accused of fraudulent reporting in 2005. Data on these firms and their contemporaries were obtained from the Taiwan Economic Journal data bank and Taiwan Stock Exchange Corporation. Financial variables were calculated for the years 2003 and 2004. Three well‐known data mining algorithms were applied to build detection/prediction models for this sample: logistic regression, neural networks, and classification trees.

Many of the variables are effective at both detecting fraudulent financial reporting and predicting business failures. In terms of overall accuracy, logistic regression outperforms the other two algorithms for detecting fraudulent financial reporting. Whether logistic regression or a decision tree is best for BFP depends on the relative opportunity cost of misclassifying failing and healthy firms.

The financial factors used to detect fraudulent reporting are helpful for predicting business failure.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

This study aims to analyze whether and how a set of financial ratios calculated on the basis of financial statement information would allow auditors of Italian local governments (LGs) to get an indication of LGs’ financial distress risk and, hence, to support politicians and managers in promptly detecting financial distress.

A model comprising a set of financial indicators that would distinguish distressed from not distressed LGs through a logistic regression approach has been estimated and applied to Italian LGs. The model is built on the basis of information pertaining to 44 distressed and 53 not distressed LGs for up to five years prior to bankruptcy and covers the period 2003-2012.

The model reveals that the percentage of personnel expenses over revenues, the turnover ratio of short-term liabilities over current revenues and the reliance on subsidies (calculated as subsidies per capita) are factors discriminating non-distressed LGs from the distressed ones.

The model could have political and practical implications. The possible use of this model as a complementary tool in auditing activities might be helpful for auditors in detecting financial distress promptly, thus potentially enabling politicians and managers to search for different ways to manage public resources to avoid the detrimental consequences related to the declaration of distress.

This model, contrary to existing models that use accrual accounting data, is applicable to LGs that adopt a modified cash accounting basis.

The purpose of this paper is to investigate the applicability of developed country turnaround predication models as well as an “in country” developed turnaround prediction model for a sample of financially distressed Malaysian companies over the period of 2000‐2007.

Multiple Discriminant Analysis (MDA) technique was used to determine companies' financial health.

It was found that severity of financial distress, profitability, liquidity and size are significant predictor variables in determining turnaround potential of distressed companies in Malaysia. The findings show that developed country turnaround predication models have relatively better prediction accuracies compared to turnaround model based on Malaysian firm‐level data. These models' prediction accuracies were gauged by comparing their predicated successful/failed turnaround companies (Type I and II errors) with actual classification of successful/failed turnaround companies by the Bursa Malaysia, and it was found that developed country models were better than model developed using Malaysian data in identifying correctly some of the actual successful turnaround companies.

The paper's comparisons show that Bursa's methodology is appropriate in classifying and monitoring the distressed companies.

This is believed to be the first paper to examine turnaround of the companies in Malaysian context.

Periodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.

In this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.

To improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.

This paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.