An adaptive neuro fuzzy inference system to model the uniaxial compressive strength of cemented hydraulic backfill

Purpose. The purpose of this paper is to develop the models for predicting the uniaxial compressive strength (UCS) of cemented hydraulic backfill (CHB), a widely used technique for filling underground voids created by mining operations as it provides the high strength required for safe and economical working environment and allows the use of waste rock from mining operations as well as tailings from mineral processing plants as ingredients. Methods. In this study, different modelling techniques such as conventional linear, nonlinear multiple regression and one of the evolving soft computing methods, adaptive neuro fuzzy inference system (ANFIS), were used for the prediction of UCS, the main criterion used to design backfill recipe. Findings. Statistical performance indices used to evaluate the efficiency of the developed models indicated that the ANFIS model can effectively be implemented for designing CHB with desired UCS. As proved by the performance indicators ANFIS model gives more compatible results with the expert opinion and current literature than conventional modelling techniques. Originality. In order to construct the models a very large database, containing more than 1600 UCS test results, was used. In addition to widely used conventional regression based modelling techniques, one of the evolving soft computing methods, ANFIS was employed. Numerical examples showing the implementation of constructed models were provided. Practical implementation. As proved by the statistical performance indicators, the developed models can be used for a reliable prediction of the UCS of CHB. However, more accurate results can be achieved by expanding the database and by constructing improved models using the algorithm presented in this paper.Мета. Побудова моделей для прогнозування межі міцності при одноосьовому стисканні цементної гідравлічної закладки для заповнення вироблених просторів шахт. Методика. Для досягнення поставленої мети були використані різні методи моделювання: лінійна та нелінійна множинна регресія, а також порівняно недавно став популярним метод програмування – адаптивне нейронечітке логічне виведення (ANFIS). За їх допомогою було спрогнозовано зміну міцності на одноосьове стискання, що є ключовим показником для визначення складу закладної суміші. Для побудови моделей використана значна база даних, яка включає результати більш ніж 1600 випробувань на одноосьове стискання. Лабораторними дослідженнями також визначалися властивості закладних матеріалів і суміші. Результати. Модель ANFIS дала найкращу продуктивність з урахуванням статистичних показників ефективності, таких як середня абсолютна процентна похибка і змінний обліковий запис. Статистичні показники продуктивності, які використовуються для оцінки ефективності розроблених моделей, свідчать, що моделювання за допомогою ANFIS дозволяє отримати результати, які більше відповідають експертній оцінці та даним з сучасної літератури, ніж інформація, отримана за допомогою традиційного моделювання. Встановлено, що на відміну від регресивного моделювання, ANFIS не вимагає заздалегідь визначених математичних рівнянь для взаємозв’язку між вхідними та вихідними змінними і використовує наданий набір даних для ефективного визначення структури моделі. Наукова новизна. Вперше для прогнозування міцності при одноосьовому стисканні були використані не лише традиційні способи моделювання, засновані на регресії, а й інноваційний метод програмування – адаптивне нейронечітке логічне виведення ANFIS. У статті наведені чисельні приклади впровадження нових побудованих моделей. Практична значимість. Статистичні індикатори продуктивності показали, що розроблені моделі можуть бути використані для надійного прогнозування міцності при одноосьовому стисканні й оптимальної рецептури закладної суміші. Однак, щоб отримати більш точні результати, необхідно мати більш широку базу даних і створити більш досконалі моделі на основі алгоритму, запропонованому в даній статті.Цель. Построение моделей для прогнозирования предела прочности при одноосном сжатии цементной гидравлической закладки для заполнения выработанных пространств шахт. Методика. Для достижения поставленной цели были использованы различные методы моделирования: линейная и нелинейная множественная регрессия, а также сравнительно недавно ставший популярным метод программирования – адаптивный нейронечеткий логический вывод (ANFIS). С их помощью было спрогнозировано изменение прочности на одноосное сжатие, что является ключевым показателем для определения состава закладочной смеси. Для построения моделей использована обширная база данных, которая включает результаты более чем 1600 испытаний на одноосное сжатие. Лабораторными исследованиями также определялись свойства закладочных материалов и смеси. Результаты. Модель ANFIS дала наилучшую производительность с учетом статистических показателей эффективности, таких как средняя абсолютная процентная погрешность и переменная учетная запись. Статистические показатели производительности, используемые для оценки эффективности разработанных моделей, свидетельствуют, что моделирование с помощью ANFIS позволяет получить результаты, которые более соответствуют экспертной оценке и данным из современной литературы, чем информация, полученная при помощи традиционного моделирования. Установлено, что в отличие от регрессионного моделирования, ANFIS не требует заранее определенных математических уравнений для взаимосвязи между входными и выходными переменными и использует предоставленный набор данных для эффективного определения структуры модели. Научная новизна. Впервые для прогнозирования прочности при одноосном сжатии были использованы не только традиционные способы моделирования, основанные на регрессии, но и инновационный метод программирования – адаптивный нейронечеткий логический вывод ANFIS. В статье приведены численные примеры внедрения новых построенных моделей. Практическая значимость. Статистические индикаторы производительности показали, что разработанные модели могут быть использованы для надежного прогнозирования прочности при одноосном сжатии и оптимальной рецептуры закладочной смеси. Однако, чтобы получить более точные результаты, необходимо иметь более широкую базу данных и создать более совершенные модели на основе алгоритма, предложенного в данной статье.The authors thank the staff and the managers of Jinfeng underground gold mine for their helps and cooperation during field and laboratory studies. The company is also acknowledged for the permission to use and publish the data

Prediction of the post-fire flexural capacity of RC beam using GA-BPNN Machine Learning

To accurately predict the flexural capacity of post-fire RC beams is imperative for fire safety design. In this paper, the residual flexural capacity of post-fire RC beams is predicted based on a back-propagation (BP) neural network (NN) optimized by a genetic algorithm (GA). First, the temperature distribution of the beams was determined using the finite element analysis software ABAQUS, and the strength reduction factor of materials was determined. The flexural capacity of the RC beams after fire is calculated by the flexural strength reduction calculation model. The model is used to generate the training data for the NN. To enable machine learning, 480 datasets are produced, of which 360 datasets are used to train the network; the remaining 120 datasets are used to test the network. The predictive models are constructed using BPNN and GA-BPNN respectively. The prediction accuracy is evaluated by comparing the predicted values and the target values. The comparison shows that the GA-BPNN has a faster convergence speed, higher stability, and can reach the goal more times, reducing the possibility of BPNN falling into the local optimum and achieving the global optimum. The proposed GA-BPNN model for predicting the flexural capacity of post-fire RC beams provides a new approach for design practice

Numerical Study of Concrete

Concrete is one of the most widely used construction material in the word today. The research in concrete follows the environment impact, economy, population and advanced technology. This special issue presents the recent numerical study for research in concrete. The research topic includes the finite element analysis, digital concrete, reinforcement technique without rebars and 3D printing

Sustainable Structural Design for High-Performance Buildings and Infrastructures

Exceptional design loads on buildings and structures may have different causes, including high-strain natural hazards, man-made attacks and accidents, and extreme operational conditions. All of these aspects can be critical for specific structural typologies and/or materials that are particularly sensitive. Dedicated and refined methods are thus required for design, analysis, and maintenance under structures’ expected lifetimes. Major challenges are related to the structural typology and material properties. Further issues are related to the need for the mitigation or retrofitting of existing structures, or from the optimal and safe design of innovative materials/systems. Finally, in some cases, no design recommendations are available, and thus experimental investigations can have a key role in the overall process. For this SI, we have invited scientists to focus on the recent advancements and trends in the sustainable design of high-performance buildings and structures. Special attention has been given to materials and systems, but also to buildings and infrastructures that can be subjected to extreme design loads. This can be the case of exceptional natural events or unfavorable ambient conditions. The assessment of hazard and risk associated with structures and civil infrastructure systems is important for the preservation and protection of built environments. New procedures, methods, and more precise rules for safety design and the protection of sustainable structures are, however, needed

Tensile and Impact Behaviour of Shape Memory Alloy Fibre Reinforced Engineered Cementitious Composites

Extreme loading events such as impact, blast, and earthquakes have often led to partial or total collapse of structures, associated with economic and human life loss. Therefore, civil engineers have been seeking innovative materials and systems that would allow designing resilient and smart structures which can withstand such catastrophic events. Recently, engineered cementitious composites (ECC) and shape memory alloys (SMA) have emerged as strong contenders in the production of smart and resilient structural systems. The aims of this study are to explore the possible synergy between ECC and SMA for developing a novel hybrid fibre-reinforced ECC incorporating randomly dispersed SMA and polyvinyl-alcohol short fibres (HECC-SMAF) with possible strain recovery and superior impact resistance. The mechanical properties of the composite, including uniaxial tensile and strain recovery performance, were examined. Moreover, the behaviour of the composite under impact loading was explored using a drop weight impact test. Test specimens were also heat-treated to investigate possible pre-stressing effects of SMA fibres on the impact resistance of the ECC. A two-parameter Weibull distribution was used to analyze variations in experimental results in terms of reliability function. Furthermore, numerical simulation was developed to predict the behaviour of the composite under impact loading. Results indicate that SMA fibres significantly enhanced the performance of the composite both under static and dynamic loading. Adding fibres beyond a certain dosage led to fibre clustering, thus, no further gain in tensile and impact performance was measured. The impact resistance of HECC-SMAF specimens was further improved after exposure to heat treatment. This highlights the significant contribution imparted by the local pre-stressing effect of SMA fibres to the impact resistance of the composite. The Weibull distribution was adequate to predict the impact failure strength of the new composite, allowing to avert additional costly experiments. Also, numerical simulation predictions of the impact behaviour of the hybrid composite were in good agreement with experimental findings, thus offering a suitable predictive tool and allowing to preclude costly and time-consuming experiments. This research underscores the potential to engineer new cementitious composites with superior tensile properties and impact resistance for the protection of critical infrastructure in the event of explosive or impact loading

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

Deflection of concrete slabs: current performance & design deflection limits

Deflection is usually controlled by limiting the span/depth ratio. One aspect of this research is to document the deflection of a concrete slab in a large residential block. The other part of the research is to look at current design limits. Limits on deformation were set many decades ago, when the forms of construction, partitions, finishing, cladding and service were very different from what they are now. Part of that is to review the span-to-depth method of design. Site investigation and testing theory through observation and data collection was the main deductive approach of this research. A quantitative method was used to calculate and determine the deflection on concrete slabs, the research is attempted to identify target companies and projects to participate in the research. The data indicate that the slab has not sagged significantly due to the back propping for 30 days. However, it does seem that the slab was sloping down from the corner by 6 mm diagonally across the 12m bay. A margin of deflection around 2mm occurred especially in the mid-span of the slab 12 x 7 m corner bay. The 2 mm deflection occurred at the beginning of the investigation after back propping reinforced concrete corner bay slab. The back propping applied after 7 days of pouring sla

Determining Additional Modulus of Subgarde Reaction Based on Tolerable Settlement for the Nailed-slab System Resting on Soft Clay.

Abstract—Nailed-slab System is a proposed alternative solution for rigid pavement problem on soft soils. Equivalent modulus of subgrade reaction (k’) can be used in designing of nailed-slab system. This modular is the cumulative of modulus of subgrade reaction from plate load test (k) and additional modulus of subgrade reaction due to pile installing (∆∆∆∆k). A recent method has used reduction of pile resistance approach in determining ∆∆∆∆k. The relative displacement between pile and soils, and reduction of pile resistance has been identified. In fact, determining of reduction of pile resistance is difficult. This paper proposes an approach by considering tolerable settlement of rigid pavement. Validation is carried out with respect to a loading test of nailed-slab models. The models are presented as strip section of rigid pavement. The theory of beams on elastic foundation is used to calculate the slab deflection by using k’. Proposed approach can results in deflection prediction close to observed one. In practice, the Nailed-slab System would be constructed by multiple-row piles. Designing this system based on one-pile row analysis will give more safety design and will consume less time