Artificial Intelligence in Civil Engineering

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering

Delay-resonance Control of Roll Press by Speed Variation Approach

Roll pairs in rolling contact are commonly used in paper machines. To improve the process at least one of the component rolls is covered by a soft layer, which is the case in calenders and coating units. The larger contact area allows longer manipulation time in the nip, and therefore faster line speeds can be achieved when using such soft covered rolls compared to hard non-covered rolls. This improvement was widely utilised in start-ups of fast next generation machines late 90’s. Several examples, however, proved that the new polymers in the cover materials were a source of new-type of oscillations arising at higher running speeds making it difficult to reach the planned production rates. As understood quite early, the reason for these instabilities is the exponential and thus too slow recover of the cover penetration during each roll revolution leading to self-regenerative normal oscillations of the roll pair. Further investigations indicated that: 1) these instabilities exist at certain discrete running speeds beyond a critical speed level, 2) the vibration frequency was always the natural frequency of the roll stack and 3) the highest amplitude peaks correspond to rotation frequencies, which are integer fractions of the natural frequency. These rotational frequencies are called delay-resonance speeds referring to the feedback mechanism of the one revolution earlier cover penetration. Dynamical instability of rolls is not accepted as it marks the roll covers by wave formations, which in turn cause variations in the line load, and deteriorates the quality of the product. Vibration also overloads mechanical structures by means of fatigue and can cause damage of vibration sensitive components. This thesis is introducing methodology and tools to control the self-excited delay-resonance vibrations at speeds, which are higher than the critical speed level making it possible to extend the production speed range. The methodology is based on systematic variation of the running speed by three different ways: 1) by setting the speed to fixed value between two successive resonance speeds, 2) by active change of the speed for avoiding the regular formation of waves on the cover and 3) by controlling phase-shift of rolls to damp cover-induced vibrations. The control methods are verified in a laboratory unit scaled down to half size of the existing industrial units

Adaptive Neuro-Fuzzy Inference System modelling of surface topology in ultra-high precision diamond turning of rapidly solidified aluminium grade (RSA 443)

Surface roughness prediction is a crucial stage during product manufacturing since it acts as a quality indicator. This investigative research thesis presents an online surface roughness prediction, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model during Ultra-High Precision Diamond Turning (UHPDT) of Rapidly Solidified Aluminium (RSA-443) using water and kerosene as coolants. Based on the Taguchi L9 orthogonal array, the cutting parameters (spindle speed, depth of cut and feed rate) are varied at three levels. Acoustic Emission (AE) signals are detected during the UHPDT process using a piezoelectric sensor. Spindle speed, depth of cut, feed rate, AE root mean square, prominent frequency and peak rate are considered as model inputs in this thesis. The experimental results reveal that a better surface finish is obtained using water coolant in comparison to kerosene coolant. Mean Absolute Percentage Error (MAPE) based comparison between ANFIS and Response Surface Method (RSM) is carried out. In this study, the ANFIS model has a prediction accuracy of 79.42% and 69.40% on water-based and kerosene-based results respectively. The RSM model yields higher prediction accuracies of 98.59% and 95.55% on water-based and kerosene-based results respectively

Adaptive Neuro-Fuzzy Inference System modelling of surface topology in ultra-high precision diamond turning of rapidly solidified aluminium grade (RSA 443)

Surface roughness prediction is a crucial stage during product manufacturing since it acts as a quality indicator. This investigative research thesis presents an online surface roughness prediction, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model during Ultra-High Precision Diamond Turning (UHPDT) of Rapidly Solidified Aluminium (RSA-443) using water and kerosene as coolants. Based on the Taguchi L9 orthogonal array, the cutting parameters (spindle speed, depth of cut and feed rate) are varied at three levels. Acoustic Emission (AE) signals are detected during the UHPDT process using a piezoelectric sensor. Spindle speed, depth of cut, feed rate, AE root mean square, prominent frequency and peak rate are considered as model inputs in this thesis. The experimental results reveal that a better surface finish is obtained using water coolant in comparison to kerosene coolant. Mean Absolute Percentage Error (MAPE) based comparison between ANFIS and Response Surface Method (RSM) is carried out. In this study, the ANFIS model has a prediction accuracy of 79.42% and 69.40% on water-based and kerosene-based results respectively. The RSM model yields higher prediction accuracies of 98.59% and 95.55% on water-based and kerosene-based results respectively

Condition monitoring of belt based motion transmission systems

A key asset of Royal Mail Group consists of a nationwide network of sorting offices that forms a constituent component of the means through which the organisation provides an efficient nationwide postal service within the United Kingdom. It may be argued that the efficiency currently possessed by modem sorting offices is due to the utilisation of machines that automate the process of sorting items of mail. The modem letter-sorting machine possessed by Royal Mail can sort up to 30,000 letters per hour; such machines serve as an example of an achievement of the application of Mechatronics. The maintenance of letter sorting machines constitutes a large overhead for the organisation. In the face of competition from pervasive electronic media within the personal communications market and the prospect of deregulation, Royal Mail seeks to streamline its operation in part by the reduction of the overheads incurred through maintenance of letter sorting machinery. The adoption of condition based maintenance techniques and predictive maintenance, for letter sorting machine components such as belts and bearings, forms part of the strategy through which Royal Mail seeks to reduce this overhead. Utilisation of flat belts and timing belts for the implementation of key functions in letter sorting machinery, such as the transportation of items of mail within the mail sorting process, results in the use of many such components within letter sorting machinery. A direct link exists between the maintenance of peak performance of a sorting machine and the maintenance of belt drives; as such the maintenance of belt drives forms a substantial component of the maintenance overhead. The focus of this thesis consists of the condition monitoring of belt based motion transmission systems and in particular, flat belts. The research that forms the basis of this thesis consists of three elements. Firstly, consideration of current knowledge of belt based power transmission such as knowledge of the mechanics of the belt based power transmission process within the context of condition monitoring... [cont'd

Dynamical systems : control and stability

Proceedings of the 13th Conference „Dynamical Systems - Theory and Applications" summarize 164 and the Springer Proceedings summarize 60 best papers of university teachers and students, researchers and engineers from whole the world. The papers were chosen by the International Scientific Committee from 315 papers submitted to the conference. The reader thus obtains an overview of the recent developments of dynamical systems and can study the most progressive tendencies in this field of science

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

【研究分野別】シーズ集 [英語版]

[英語版

Novel control of a high performance rotary wood planing machine

Rotary planing, and moulding, machining operations have been employed within the woodworking industry for a number of years. Due to the rotational nature of the machining process, cuttermarks, in the form of waves, are created on the machined timber surface. It is the nature of these cuttermarks that determine the surface quality of the machined timber. It has been established that cutting tool inaccuracies and vibrations are a prime factor in the form of the cuttermarks on the timber surface. A principal aim of this thesis is to create a control architecture that is suitable for the adaptive operation of a wood planing machine in order to improve the surface quality of the machined timber. In order to improve the surface quality, a thorough understanding of the principals of wood planing is required. These principals are stated within this thesis and the ability to manipulate the rotary wood planing process, in order to achieve a higher surface quality, is shown. An existing test rig facility is utilised within this thesis, however upgrades to facilitate higher cutting and feed speeds, as well as possible future implementations such as extended cutting regimes, the test rig has been modified and enlarged. This test rig allows for the dynamic positioning of the centre of rotation of the cutterhead during a cutting operation through the use of piezo electric actuators, with a displacement range of ±15μm. A new controller for the system has been generated. Within this controller are a number of tuneable parameters. It was found that these parameters were dependant on a high number external factors, such as operating speeds and run‐out of the cutting knives. A novel approach to the generation of these parameters has been developed and implemented within the overall system. Both cutterhead inaccuracies and vibrations can be overcome, to some degree, by the vertical displacement of the cutterhead. However a crucial information element is not known, the particular displacement profile. Therefore a novel approach, consisting of a subtle change to the displacement profile and then a pattern matching approach, has been implemented onto the test rig. Within the pattern matching approach the surface profiles are simplified to a basic form. This basic form allows for a much simplified approach to the pattern matching whilst producing a result suitable for the subtle change approach. In order to compress the data levels a Principal Component Analysis was performed on the measured surface data. Patterns were found to be present in the resultant data matrix and so investigations into defect classification techniques have been carried out using both K‐Nearest Neighbour techniques and Neural Networks. The application of these novel approaches has yielded a higher system performance, for no additional cost to the mechanical components of the wood planing machine, both in terms of wood throughput and machined timber surface quality