Search

Introduction: Landslides pose significant geological hazards, necessitating advanced prediction techniques to protect vulnerable populations. Research Gap: Reviewing landslide time series analysis predictions is found to be missing despite the availability of numerous reviews. Methodology: Therefore, this paper systematically reviews time series analysis in landslide prediction, focusing on physically based causative models, highlighting data preparation, model selection, optimizations, and evaluations. Key Findings: The review shows that deep learning, particularly the long-short-term memory (LSTM) model, outperforms traditional methods. However, the effectiveness of these models hinges on meticulous data preparation and model optimization. Significance: While the existing literature offers valuable insights, we identify key areas for future research, including the impact of data frequency and the integration of subsurface characteristics in prediction models. [ABSTRACT FROM AUTHOR]

Landslides are frequent hillslope events that may present significant risks to humans and infrastructure. Researchers have made ongoing efforts to assess the potential danger associated with landslides, intending to ascertain the location, frequency, and magnitude of these events in a given area. This study is meant to supplement the previous study (Part I), which explored empirical and physically based causative thresholds. In this paper (Part II), a systematic review is used to conduct an in-depth study of existing research on prediction models. Deterministic physical approaches were investigated for local-scale landslides. Next, national-scale landslide susceptibility models are discussed, including qualitative and quantitative models. Consequently, key findings about rainfall-induced landslides are reviewed. The strategy selection is generally governed by data and input factors from a macroscopic perspective, while the better prediction model is defined by dataset quality and analysis model performance from a microscopic perspective. Physically based causative thresholds can be used with limited geotechnical or hydrological data; otherwise, numerical analysis provides optimal accuracy. Among all statistical models, the hybrid artificial intelligence model achieved the best accuracy. Finally, current challenges have concentrated on integrating AI and physical models to obtain high accuracy with little data, prompting research suggestions. Advanced constitutive models for real-time situations are lacking. Dynamic and spatiotemporal susceptibility maps are also used, although their subjectivity needs further research. This study analyses how to choose the best model and determine its key traits. This research provides valuable insights for scholars and practitioners seeking innovative approaches to lessen the severity of landslides. [ABSTRACT FROM AUTHOR]

Corrosion is still the most common contributor to failures in Water Distribution Networks (WDNs), causing detrimental techno-socio-economic impacts. Although the corrosion process has been the subject of several studies, factors influencing this process remain a source of contention due to the complexity of the process and its influence by the surrounding environment. Considering the prior reviews, this comprehensive review is considered an early attempt to thoroughly cover the most influential corrosion factors in water pipelines. Corrosion factors have been classified into three main categories: 1) environmental factors; soil factors, external factors, and stray current factors; 2) pipe-related factors, and 3) operational factors. A fault tree analysis diagram was used to map, discuss, and analyze all significant corrosion causes of the buried water pipelines to facilitate easy visualization from basic factors to their intermediate and parent factors. Furthermore, the techno-socio-economic impacts of corrosion on water pipelines and beyond are appropriately addressed to demonstrate the issue's multi-dimensional importance. The research is expanded to rank these factors using the fuzzy analytical hierarchy process to provide a better understanding of the currently focused research investigation and to enable the extraction of gaps and existing limitations in scholarly literature. The findings revealed that water quality is the most investigated factor, followed by electrical infrastructure and soil quality. Conversely, operational factors exhibit the greatest relative weight (0.428), followed by environmental factors (0.337). These findings highlight areas where further research is needed, and the article proposes potential directions for future studies to address these gaps. [ABSTRACT FROM AUTHOR]

The growing complexity and dynamism of construction projects have ultimately elevated the importance of project delivery systems. In addition, working in post-conflict environment has further magnified their significance. The literature review lacks a synthesized framework that is designed for the selection of project delivery systems in post-conflict construction projects. Accordingly, the overarching objective of this research is to develop a well-rounded framework for structured selection of the most suitable project delivery system (PDS) in post-conflict construction projects. The developed model encompasses two approaches for PDS selection, namely individual based and consensus based. These approaches differ in their modality of judgment elicitation and score aggregation but coincide in the output which is the suitability index for the PDS options. The constructed suitability index simulates both the relative importance weights of the most significant PDS factors alongside their relative effectiveness values. In this regard, it harnesses analytical network process to compute the relative importance weights of the PDS selection factors. The first approach is predicated on individual assessment of the parameters leading to calculation of the suitability indices. This approach applies Monte Carlo simulation to define a range for the suitability indices. The second approach, however, is hinged upon consensus-based assessment of the components of the suitability index through conferencing workshops. Comparative analysis demonstrated that construction manager at risk is the most attractive PDS option in post-conflict Afghanistan. In addition, design-build is a more suitable PDS option than design-bid-build for construction of health/educational and office/government projects in post-conflict Afghanistan. It is also manifested that project cost, security constraint and political impact, availability of experienced contractors sustain the highest significance on the suitability index in health and educational buildings. Furthermore, availability of resources and material, agency's in-house capacity, and availability of experienced contractors sustain the most paramount implication on the suitability index of construction manager at risk in office and government projects. It is anticipated that the developed integrated model could aid development agencies in selecting the most appropriate project delivery system that is best suited to their projects on the basis of project objectives, priorities, and location conditions. [ABSTRACT FROM AUTHOR]

Quantifying failure events of oil and gas pipelines in real- or near-real-time facilitates a faster and more appropriate response plan. Developing a data-driven pipeline failure assessment model, however, faces a major challenge; failure history, in the form of incident reports, suffers from limited and missing information, making it difficult to incorporate a persistent input configuration to a supervised machine learning model. The literature falls short on the development of appropriate solutions to utilize incomplete databases and incident reports in the pipeline failure problem. This work proposes a semi-supervised machine learning framework which mines existing oil and gas pipeline failure databases. The proposed cluster-impute-classify (CIC) approach maps a relevant subset of the failure databases through which missing information in the incident report is reconstructed. A classifier is then trained on the fly to learn the functional relationship between the descriptors from a diverse feature set. The proposed approach, presented within an ensemble learning architecture, is easily scalable to various pipeline failure databases. The results show up to 91% detection accuracy and stable generalization ability against increased rate of missing information. [ABSTRACT FROM AUTHOR]

Accelerometers are a powerful tool when it comes to leak detection, especially in small pipelines. The increase in accelerometer research related to leak pinpointing and detection has identified that vibration signals dissipate in an inconstant fashion based on the type and size of a pressurized water pipeline. This article presents a sensor system-specific approach to measure the loss in vibration signal from the source over the length of a given pipeline. The experimental work in this article spans one-inch and two-inch PVC pipelines as well as two-inch ductile iron pipelines. Experiments were carried out under controlled conditions to gather data. The collected signal data are analyzed and applied to a signal decay assessment model. Signal decay was significantly higher in ductile iron pipelines than in PVC pipelines. Additionally, two-inch pipelines possessed a higher decay per meter rating than one-inch pipelines, which indicates that diameter size affects signal decay as well. Finally, the results of the signal decay are then used in an example to propose a sensor distribution method based on the given medium where the sensor and the pipeline are placed. [ABSTRACT FROM AUTHOR]

Existing bridges are aging and deteriorating. Furthermore, large number of bridges exist in transportation networks meanwhile maintenance budgets are being squeezed. This state of affairs necessities the development of automatic bridge defects evaluation model using computer vision technologies to overcome the limitations of visual inspection. The digital images are prone to degradation by noises during the image acquisition phase. The absence of efficient bridge defects image restoration method results in inaccurate condition assessment models and unreliable bridge management systems. The present study introduces a self-adaptive two-tier method for detection of noises and restoration of bridge defects images. The first model adopts Elman neural network coupled with invasive weed optimization algorithm to identify the type of noise that corrupts images. In the second model, moth-flame optimization algorithm is utilized to design a hybrid image filtering protocol that involves an integration of spatial domain and frequency domain filters. The proposed detection model was assessed through comparisons with other machine learning models as per split validation and tenfold cross validation. It attained the highest classification accuracies, whereas the accuracy, sensitivity, specificity, precision, F-measure and Kappa coefficient are 95.28%, 95.24%, 98.07%, 95.25%, 95.34%. 95.43% and 0.935, respectively in the separate noise recognition module. The capabilities of the proposed restoration model were evaluated against some well-known good-performing optimization algorithms in addition to some conventional restoration models. Moth-flame optimization algorithm outperformed other restoration models, whereas peak signal to noise ratio, mean-squared error, normalized absolute error and image enhancement factor are 25.359, 176.319, 0.0585 and 7.182, respectively. [ABSTRACT FROM AUTHOR]

Background: Carpal tunnel syndrome (CTS) is the most common peripheral nerve entrapment syndrome worldwide. Objective: The current work aimed at determining the effect of extracorporeal shock wave therapy (ESWT) on different severities of carpal tunnel syndrome. Subjects and methods: Sixty patients with unilateral carpal tunnel syndrome were participated in this study. They were divided into three equal groups according to severity (mild, moderate, and severe). All patients received shock wave therapy one session per week, for 6 weeks. Nerve conduction studies (NCs) were done for the median nerve. Pinch dynamometer was used to measure the degree of lateral pinch power, and Visual Analogue Scale (VAS) was used to assess degree of pain and Ultrasonography analysis measured cross-sectional area (CSA) of median nerve for all patients at the beginning and at the end of the study. Results: There was significant improvement (P < 0.05) in sensory conduction parameters, CSA, lateral pinch power, and VAS after shock wave therapy in all groups. Also, there was significant improvement in motor latency, motor, and sensory amplitudes after shock wave therapy in mild and moderate groups only (P < 0.05). In all group samples, results revealed a highly significant correlations (p < 0.001) between peak sensory latency (PSL) with VAS, CSA with motor latency (ML), CSA with PSL, CSA with VAS, CSA with lateral pinch, and ML with lateral pinch. Conclusion: It is recommended to use ESWT as a conservative treatment in patients with different severities of CTS, except patients with severe motor affection. Trial registration: PACTR201804003293116. Registered on 26 April 2018 as retrospective registration in the pan African clinical trial registry (www.pactr.org). [ABSTRACT FROM AUTHOR]

Existing infrastructure is aging, while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges are subjected to severe deterioration agents such as variable traffic loading, deferred maintenance, cycles of freeze and thaw. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays due to the huge variance between the need for maintenance actions and the available funds to perform such actions. Condition assessment is regarded as one of the most critical and vital components of BMSs. Ground penetrating radar (GPR) is one of the nondestructive techniques that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. There is a major issue associated with the GPR which is the absence of a scale for the amplitude values. The objective of the proposed model is to compute standardized amplitude thresholds for corrosion maps. The proposed model considers eight un-supervised clustering algorithms to obtain the thresholds. The proposed model incorporates a multi-objective optimization-based methodology that employs three evolutionary optimization algorithms to calculate the optimum thresholds which are: (1) genetic algorithm, (2) particle swarm optimization algorithm, and (3) shuffled frog-leaping algorithm. Five multi-criteria decision-making techniques are used to provide a ranking for the solutions. Finally, group decision-making is performed to aggregate the results and obtain a consensus and compromise solution. The standardized thresholds obtained from the proposed methodology are: − 16.7619, − 8.8161, and − 2.9744 dB. [ABSTRACT FROM AUTHOR]

Water mains, especially old pipelines, are consistently threatened by the formation of leaks. Leaks inherit increased direct and indirect costs and impacts on various levels such as the economic field and the environmental level. Recently, financially capable municipalities are testing acoustic early detection systems that utilize wireless noise loggers. Noise loggers would be distributed throughout the water network to detect any anomalies in the network. Loggers provide early detection via recording and analyzing acoustic signals within the network. The city of Montreal adopted one of the leak detection projects in this domain and had reported that the main issue that hinders the installed system is false alarms. False alarms consume municipality resources and funds inefficiently. Therefore, this paper aims to present a novel approach to utilize more than one data analysis and classification technique to ameliorate the leak identification process. In this research, acoustic leak signals were analyzed using Fourier Transform, and the multiple frequency bandwidths were determined. Three models were developed to identify the state of the leak using Naïve Bayes (NB), Deep Learning (DL), and Decision Tree (DT) Algorithms. Each of the developed models has an accuracy ranging between 84% to 89%. An aggregator approach was developed to cultivate the collective approaches developed into one single answer. Through aggregation, the accuracy of leak detection improved from 89% at its best to 100%. The design, implementation approach and results are displayed in this paper. Using this method helps municipalities minimize and alleviate the costs of uncertain leak verifications and efficiently allocate their resources. [ABSTRACT FROM AUTHOR]

Leaks in resource transmission pipelines is a growing concern for the water transmission industry. This creates a need to prevent the threat of leaks and minimize their damages through extensive research in leak detection technology. This research work provides a thorough investigation into the history of leak detection in pipelines by surveying the web of knowledge database and visualizing the outputs using visualization software VOSviewer and CiteNetExplorer. The analysis of the web of knowledge output presents a set of the ten most used keywords in the field of leak detection in pipelines that are further described and analyzed. Additionally, in-depth analysis of a randomly selected sample of papers was conducted to draw a sense of the progress in the industry over the past four decades. This article also defines a novel approach to define the leak detection phases, i.e., the identify-localize-pinpoint approach. Furthermore, two classes of leak detection systems are identified, static leak detection systems and dynamic leak detection systems. The two systems are defined as well as their differentiative capabilities. Finally, this article provides a summary of popular leak detection technologies to provide a broad sense of understanding for the leak detection field of research. [ABSTRACT FROM AUTHOR]