BIM-based software for construction waste analytics using artificial intelligence hybrid models

The Construction industry generates about 30% of the total waste in the UK. Current high landfill cost and severe environmental impact of waste reveals the need to reduce waste generated from construction activities. Although literature reveals that the best approach to Construction Waste (CW) management is minimization at the design stage, current tools are not robust enough to support architects and design engineers. Review of extant literature reveals that the key limitations of existing CW management tools are that they are not integrated with the design process and that they lack Building Information Modelling (BIM) compliance. This is because the tools are external to design BIM tools used by architects and design engineers. This study therefore investigates BIM-based strategies for CW management and develops Artificial Intelligent (AI) hybrid models to predict CW at the design stage. The model was then integrated into Autodesk Revit as an add-in (BIMWaste) to provide CW analytics. Based on a critical realism paradigm, the study adopts exploratory sequential mixed methods, which combines both qualitative and quantitative methods into a single study. The study starts with the review of extant literature and (FGIs) with industry practitioners. The transcripts of the FGIs were subjected to thematic analysis to identify prevalent themes from the quotations. The factors from literature review and FGIs were then combined and put together in a questionnaire survey and distributed to industry practitioners. The questionnaire responses were subjected to rigorous statistical process to identify key strategies for BIM-based approach to waste efficient design coordination. Results of factor analysis revealed five groups of BIM strategies for CW management, which are: (i)improved collaboration for waste management, (ii)waste-driven design process and solutions, (iii)lifecycle waste analytics, (iv) Innovative technologies for waste intelligence and analytics, and (v)improved documentation for waste management. The results improve the understanding of BIM functionalities and how they could improve the effectiveness of existing CW management tools. Thereafter, the key strategies were developed into a holistic BIM framework for CW management. This was done to incorporate industrial and technological requirements for BIM enabled waste management into an integrated system.The framework guided the development of AI hybrid models and BIM based tool for CW management. Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed for CW prediction and mathematical models were developed for CW minimisation. Based on historical Construction Waste Record (CWR) from 117 building projects, the model development reveals that two key predictors of CW are “GFA” and “Construction Type”. The final models were then incorporated into Autodesk Revit to enable the prediction of CW from building designs. The performance of the final tool was tested using a test plan and two test cases. The results show that the tool performs well and that it predicts CW according to waste types, element types, and building levels. The study generated several implications that would be of interest to several stakeholders in the construction industry. Particularly, the study provides a clear direction on how CW management strategies could be integrated into BIM platform to streamline the CW analytics

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment

Risk-Based Maintenance Planning Model for Oil and Gas Pipelines

Oil and gas pipelines are the main means of transporting fossil fuels from the wellheads and processing facilities to the distribution centers. The 2013 US infrastructure report card assigned a grade of D+ to energy pipelines signifying they are in a poor condition. More than 10,000 incidents were reported on oil and gas pipelines during the last two decades, most of which resulted in considerable consequences. Recent failures and ruptures have raised concerns over the risk of failure of such pipes in Canada. The main objective of this research is to develop a risk-based maintenance planning model for oil and gas pipelines. The research develops a probability of failure (POF) and a consequence of failure (COF) prediction model and establishes a risk-based inspection and simulation-based rehabilitation planning models. The POF model develops a comprehensive index by applying the granular theory of uncertainty and the principles of probability theory to forecast the POF of oil and gas pipes. The neuro-fuzzy technique is employed to develop a model that forecasts the financial consequences of the potential failures of such pipes. An integrated fuzzy risk evaluation model is developed with 25 fuzzy rules to assess a pipeline’s risk index. A fuzzy expert system is developed to select the inspection tools and determine their run-frequency according to the failure risk of a pipeline. Regression analysis is applied to develop a risk growth profile to forecast the maximum failure risk of various inspection scenarios. Scenarios are ranked based on their risk-cost index, which integrates two main indices: 1) maximum risk of failure, and 2) life cycle cost of scenarios, computed by applying the Monte-Carlo simulation. Finally, a comprehensive maintenance model proposes the optimum maintenance plans with the lowest LCC, developing a third-degree risk-based deterioration profile of the pipelines. The POF model’s sensitivity results highlight that cathodic protection effectiveness and soil resistivity are the leading causes of external corrosion failures, while the depth of cover is an important factor of mechanical damages. The COF model attests that diameter, as well as the location properties are important factors for estimating the financial consequences. The developed risk assessment model is validated using a test dataset that proved the models are accurate with about 80% validity. The developed models are applied on a case study of a 24-inch pipe. The POF and COF of the pipe are computed, and the results suggest that the pipe’s risk index is above medium with an average index of 3.5. The study proposes the application of an inspection tool, which decreases the risk growth by 50% during the service life of the pipeline. The application of the maintenance planning model proposes a combination of recoat, repair, and replacement with a medium size of rehabilitation. The net present value of the proposed scenario of maintenance is estimated to cost around 1.7 million dollars over the life cycle of the pipeline, compared to the last-ranked alternative that costs over three million dollars. This research offers a framework to develop a comprehensive index to predict the failure risk of pipes using historical data that can be extended to the other infrastructure types. It develops a model to plan for the optimal pipeline maintenance, and provides an overall image of its service life. The developed models will help the operators predict the risk of failure and plan appropriately for the life cycle of their oil and gas pipelines

The Latest Scientific Problems Related to the Implementation and Diagnostics of Construction Objects

This book contains publications related to the special topic entitled: "The Latest Scientific Problems Related to the Implementation and Diagnostics of Construction Objects". Construction is a sector of the economy that is characterized by a very high variability of execution conditions and a large variety of building structures. In a period of very rapid economic development, this high variability and diversity generates many new scientific problems that must be solved in order to further improve the quality of production, as well as to reduce the cost and time of construction. The purpose of the issue is to present and discuss the results of the latest research in the broad field of construction engineering, particularly concerning: modification of the composition of construction materials using various micro- and nanomaterials, by-products or wastes; modern methods of controlling construction processes; methods of planning and effective management in construction, as well as methods of diagnosing construction objects. The articles published in this issue deal with theoretical, experimental, applied and modeling research conducted worldwide in the above-mentioned scientific areas

Data-driven method for enhanced corrosion assessment of reinforced concrete structures

Corrosion is a major problem affecting the durability of reinforced concrete structures. Corrosion related maintenance and repair of reinforced concrete structures cost multibillion USD per annum globally. It is often triggered by the ingression of carbon dioxide and/or chloride into the pores of concrete. Estimation of these corrosion causing factors using the conventional models results in suboptimal assessment since they are incapable of capturing the complex interaction of parameters. Hygrothermal interaction also plays a role in aggravating the corrosion of reinforcement bar and this is usually counteracted by applying surface protection systems. These systems have different degree of protection and they may even cause deterioration to the structure unintentionally. The overall objective of this dissertation is to provide a framework that enhances the assessment reliability of the corrosion controlling factors. The framework is realized through the development of data-driven carbonation depth, chloride profile and hygrothermal performance prediction models. The carbonation depth prediction model integrates neural network, decision tree, boosted and bagged ensemble decision trees. The ensemble tree based chloride profile prediction models evaluate the significance of chloride ingress controlling variables from various perspectives. The hygrothermal interaction prediction models are developed using neural networks to evaluate the status of corrosion and other unexpected deteriorations in surface-treated concrete elements. Long-term data for all models were obtained from three different field experiments. The performance comparison of the developed carbonation depth prediction model with the conventional one confirmed the prediction superiority of the data-driven model. The variable importance measure revealed that plasticizers and air contents are among the top six carbonation governing parameters out of 25. The discovered topmost chloride penetration controlling parameters representing the composition of the concrete are aggregate size distribution, amount and type of plasticizers and supplementary cementitious materials. The performance analysis of the developed hygrothermal model revealed its prediction capability with low error. The integrated exploratory data analysis technique with the hygrothermal model had identified the surfaceprotection systems that are able to protect from corrosion, chemical and frost attacks. All the developed corrosion assessment models are valid, reliable, robust and easily reproducible, which assist to define proactive maintenance plan. In addition, the determined influential parameters could help companies to produce optimized concrete mix that is able to resist carbonation and chloride penetration. Hence, the outcomes of this dissertation enable reduction of lifecycle costs

Building exterior paint performance in tropical salty environment of Lagos

Materials that are used for exterior finishes of buildings are their last line of defence against the elements of climate. The compliance of the materials with the environment will determine their performance and consequent repainting. A high percentage of buildings? exterior surfaces are finished with paint in Lagos in the south-western region of Nigeria that has additional climatic stress of saline air from the Atlantic Ocean. This research examines the factors that influence the use of paint and the occurrence of salt impact indicators as parameters for measuring its performance in Lagos, Nigeria, in order to determine the repainting requirement when applied as exterior finish. A mixed approach of qualitative and quantitative methods was used. Survey was done to collect data for the analysis. The study area was bifurcated into two zones (Zone A & B) in order to compare the performance of painted exterior of the buildings along the coast with the hinterland. After exhaustive literature search, variables identified were used to investigate the phenomenon. While some variables were measured using Likert?s scale, some were simply measured through categorical and continuous scales. A total of 317 questionnaires were returned out of the 384 sample size of the first target population. Guided interview was conducted on sixteen (16) architects and six (6) paint manufacturers provided information on salt resistant paint of the second and third target population respectively. Various appropriate statistical models were used with the significance level set at p<0.05 in processing the data to obtain results for inferences. Findings show that the choice and usage of paint as exterior finish for buildings in the study area depend on experience, professional advice, availability, cost, societal influence and cultural values. The performance and climatic compliance are jettisoned because of cultural attachment to colour that paints make available with relative ease. All salt impact indicators appeared earlier in Zone A than Zone B. These also necessitate earlier and frequent repainting in Zone A. The number of years required for repainting in Zone A and Zone B are 2 and 4 years respectively compared with 5-7 years established in past researches as required for repainting in other parts of the tropical region. The outcome of the research facilitates maintenance forecast and schedule for painted exterior in the tropics

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808