Estimating dynamic origin–destination demand: A hybrid framework using license plate recognition data

This article proposes a hybrid framework for estimating dynamic origin–destination (OD) demand that fully exploits the information available in license plate recognition (LPR) data. A Bayesian path reconstruction model is initially developed to replenish the lost information resulting from the recognition error and insufficient coverage rate of the LPR system. The link flows, initial OD demand, left‐turning flows, and partial path flows are derived based on the reconstructed data. Subsequently, with the information derived, a two‐step ordinary least squares (OLS) OD estimation model is formulated, which incorporates the output from the Bayesian model and coestimates the OD demand and assignment matrix. The proposed framework is qualitatively validated using the real‐world LPR data collected from Langfang City, Hebei Province, China, and is quantitatively validated using the synthesized simulation data for the simplified road network of Langfang. The results show that the proposed model can estimate OD demand distribution with a mean absolute percentage error (MAPE) of about 30%. We also tested the model with different LPR coverage rates, with results showing that an LPR coverage rate of over 50% is required to obtain reasonable results.     

A dynamic stiffness-based framework for harmonic input estimation and response reconstruction considering damage

In structural health monitoring (SHM), the measurement is point-wise but structures are continuous. Thus, input estimation has become a hot research subject with which the full-field structural response can be calculated with a finite element model (FEM). This paper proposes a framework based on the dynamic stiffness theory, to estimate harmonic input, reconstruct responses, and to localize damages from seriously deficient measurements. To begin, Fourier transform converts the dynamic equilibrium equation to an equivalent static one in the frequency domain, which is under-determined since the dimension of measurement vector is far less than the FEM-node number. The principal component analysis has been adopted to “compress” the under-determined equation, and formed an over-determined equation to estimate the unknown input. Then, inverse Fourier transform converts the estimated input in the frequency domain to the time domain. Applying this to the FEM can reconstruct the target responses. If a structure is damaged, the estimated nodal force can localize the damage. To improve the damage-detection accuracy, a multi-measurement-based indicator has been proposed. Numerical simulations have validated that the proposed framework can capably estimate input and reconstruct multi-types of full-field responses, and the damage indicator can localize minor damages even with the existence of noise.     

Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach

Dynamic origin‐destination (OD) flow estimation is one of the most fundamental problems in traffic engineering. Despite numerous existing studies, the OD flow estimation problem remains challenging, as there is large dimensional difference between the unknown values to be estimated and the known traffic observations. To meet the needs of active traffic management and control, accurate time‐dependent OD flows are required to understand time‐of‐day traffic flow patterns. In this work, we propose a three‐dimensional (3D) convolution‐based deep neural network, “Res3D,” to learn the high‐dimensional correlations between local traffic patterns presented by automatic vehicle identification observations and OD flows. In this paper, a practical framework combining simulation‐based model training and few‐shot transfer learning is introduced to enhance the applicability of the proposed model, as continuously observing OD flows could be expensive. The proposed model is extensively tested based on a realistic road network, and the results show that for significant OD flows, the relative errors are stable around 5%, outperforming several other models, including prevalent neural networks as well as existing estimation models. Meanwhile, corrupted and out‐of‐distribution samples are generated as real‐world samples to validate Res3D's transferability, and the results indicated a 60% improvement with few‐shot transfer learning. Therefore, this proposed framework could help to bridge the gaps between traffic simulations and empirical cases.     

An integrated topology optimization framework for three‐dimensional domains using shell elements

In the last decades, topology optimization has been widely investigated as a preliminary design tool to minimize the use of material in a structure. Despite this, applications to realistic three‐dimensional engineering problems are still limited. This study provides the instruments for the definition of a versatile and integrated framework in order to apply topology optimization to large‐scale 3‐D domains for the design of efficient and high‐performing structures. The paper proposes a novel topology optimization strategy to identify the optimal layout of lateral resisting systems for tall buildings through the adoption of Mindlin–Reissner shell elements for the discretization of the continuum design domain. The framework is based on the practical interoperability between MATLAB, Ansys, and computer‐aided design (CAD) environments to incorporate optimization routines in the conceptual design phase of structural systems. Finally, the paper examines a three‐dimensional tall building case study in order to demonstrate the applicability of the proposed procedure to realistic Civil Engineering design problems and its robustness in finding optimal layouts free from mesh‐dependency instabilities.     

Internal force monitoring and estimation of a long‐span ring beam using long‐gauge strain sensing

The internal force information of curved beam members is important evidence for the stress state evaluation and design verification of long‐span space structure during construction. Many structural analysis methods for curved beams have been proposed, but most are used to handle the complex geometric shapes. For stress state evaluation of long‐span steel‐concrete composite (SCC) ring beam, this article presents a method that employs a sectional strain distribution model (SSDM) and a fiber model to estimate the internal force distribution depending on the long‐gauge strain. The SSDM of the ring beam used in the article is based on a mechanical model of two‐dimensional (2‐D) plane bending deformation and a three‐dimensional (3‐D) solid finite element model of curved beam members. The application range of the SSDM is defined by the divided different stress areas of ring beam based on the 3‐D solid finite element model. Combined with the established SSDM and the different stress areas, the axial force and bending moment along the span are then identified separately based on the fiber model. In consideration of field monitoring, the influence of different sensor layout on the establishment of SSDM and the result of internal force identification is discussed. The results of the numerical studies show that the proposed methodology can identify the internal force distribution accurately. Internal force of long‐span ring beam from Jiangsu Grand Theater during construction is also identified through the proposed method by using the strain monitoring data.     

Simulation‐based dynamic traffic assignment: Meta‐heuristic solution methods with parallel computing

The aim of this study is to solve the large‐scale dynamic traffic assignment (DTA) model using a simulation‐based framework, which is computationally a challenging problem. Many studies have been performed on developing an efficient algorithm to solve DTA. Most of the existing algorithms are based on path‐swapping descent direction methods. From the computational standpoint, the main drawback of these methods is that they cannot be parallelized. This is because the existing algorithms need to know the results of the last iteration to determine the next best path flow for the next iteration. Thus, their performance depends on the single initial or intermediate solution, which means they exploit a solution that satisfies the equilibrium conditions more than explore the solution space for the optimal solution. More specifically, the goal of this study is to overcome the drawbacks of serial algorithms by using meta‐heuristic algorithms known to be parallelizable and that have never been applied to the simulation‐based DTA problem. This study proposes two new solution methods: a new extension of the simulated annealing and an adapted genetic algorithm. With parallel simulation, the algorithm runs more simulations in comparison with existing methods, but the algorithm explores the solution space better and therefore obtains better solutions in terms of closeness to the optimal solution and computation time compared to classical methods.     

Shear strength estimation of reinforced concrete beam–column sub‐assemblages using multiple soft computing techniques

The aim of the present study is to propose innovative predictive models for shear capacity of reinforced concrete (RC) exterior joints in terms of multiple soft computing techniques. Existing models were evaluated and by a preliminary sensitivity analysis, seven parameters including compressive strength of concrete, product of the yield stress and the reinforcement ratio of the joint stirrups, the effective width of the joint panel, cross‐sectional column width, beam tensile longitudinal reinforcement ratio, beam compressive longitudinal reinforcement ratio, and column longitudinal reinforcement ratio were considered. Then, a large data set having the details of experimental programs on shear capacity of exterior RC beam–column joints was provided. The experimental data were utilized in developing the proposed models. After verification of the new models against available database, their efficiency compared with existing models was confirmed. Finally, a sensitivity analysis was performed in order to find the relative importance of each input parameter on the shear strength of RC joints. The results indicated that the beam reinforcement is the most important factor in shear capacity estimation of exterior RC beam–column connections.     

Comprehensive nonlinear seismic performance assessment of MR damper controlled systems using virtual real‐time hybrid simulation

Magnetorheological (MR) dampers have gained significant attention in seismic mitigation of structural systems due to their distinguished characteristics such as inherent stability and minimum power requirements. Their performance in control of nonlinear structural response, however, has not been widely investigated. This paper provides comprehensive nonlinear seismic performance assessment of a three‐story benchmark structure equipped with a large‐scale MR damper using virtual real‐time hybrid simulation to efficiently capture the nonlinear behavior of the damper. The framework is first verified by means of available experimental results of an actual RTHS on the same structural system. A set of 12 earthquake ground motions, each one scaled to have 12 different intensities are then utilized to perform nonlinear dynamic analyses. An energy‐based adaptive passive‐on control strategy is proposed, and its performance is compared with passive‐on, passive‐off, and uncontrolled response of the structure in terms of interstory drifts shown by fragility curves, residual drifts, MR damper control force, and the ability to maintain a uniform interstory drift along the height of the structure.     

Structural design of high‐rise buildings using steel‐framed modules: A case study in Hong Kong

Steel‐framed modular buildings afford certain advantages, such as rapid and high‐quality construction. However, although steel‐framed modules have been adopted in several countries, most of them are limited to low‐to‐medium‐rise structures; modular high‐rise buildings are rare. This study proposes a feasible structural design solution for high‐rise buildings using a steel‐framed modular system. A 31‐story student hostel building in Hong Kong is redesigned as a steel‐framed modular building and used as a case study. The finite element models of the building are formulated, and the structural behaviors under wind and earthquake load scenarios are compared. Moreover, the structural design process used for the 31‐story building is applied to design a hypothetical 40‐story modular building to further examine the proposed design solution. The numerical analysis results indicate that the roof lateral displacements and interstory drift ratios of the redesigned modular building are within the allowable limits of design codes; moreover, the modular connections behave elastically under the most adverse loading scenarios. Accordingly, the proposed solution can be used to design steel‐framed modular buildings of up to 40 stories, while complying with relevant wind and seismic codes.     

Surface runoff estimation over heterogeneous foothills of Aravalli mountain using medium resolution remote sensing rainfall data with soil conservation system‐curve number method: A case of semi‐arid ungauged Manesar Nala watershed

Manesar Nala watershed, having an aerial extent of 71.53 km², was subjected to modelling of its hydrological behaviour for assessing its water resource potential. Modern tools and techniques of Remote Sensing (RS) and Geographic Information System (GIS) were used for assessment of runoff generating potential using the Hydrologic Soil Cover Complex (HSCC) Method [U.S. Department of Agriculture (USDA)‐Soil Conservation System‐Curve Number (SCS‐CN) approach]. RS and GIS were used in generation and integration of thematic maps [such as Land use/Land cover (using LISS‐III data) and Hydrologic Soil Group (HSG), (using soil map of study area) to derive the Curve Number (CN) for simulating Runoff (R_o)]. The daily rainfall (P) data for the study period 2002–2015 were acquired from NOAA Climate Prediction Center (NCPC). Corresponding R_o from the watershed for intense storm events for 14 years were calculated through RS and GIS. GIS and SCS‐CN model was employed for modelling the runoff production to study its hydrological behaviour. The study showed that the Manesar Nala watershed was having a composite Curve Number – II (CN_II) value of 82.5 for normal conditions. For dry and wet conditions these values were estimated at 66.44 (CN_I) and 91.56 (CN_III), respectively. This investigation showed that Manesar Nala watershed exhibited an annual average (of 14 years, 2002–2015) R_o volume of 4 542 514.37 m³ based on the average annual rainfall (P) of 0.72 m (720 mm). The average annual surface runoff (R_o) was predicted to be approximately 0.21 m with annual runoff coefficient (C_R) of 0.29. During the study, we also found a strong correlation ‘r’ between satellite driven P and R_o from NRCS‐CN method of the order of 0.94. The methodology so developed has the potential to be used in other similar ungauged watersheds in the same agro‐climatic conditions for the purpose of planning of watershed conservation measures and other developmental activities.     

Seismic assessment of rc buildings by estimation of effective parameters on seismic behaviour using non‐destructive tests

Seismic assessment of existing buildings is needed in many cases: change in construction usage (change in applied loads), undesirable construction quality, disparity between standard specimen and technical specification of design, absence of construction plans and modification of codes. Seismic assessment is the first step towards rehabilitation of constructions or other schemes to decrease damages and personal injuries. In this regard, engineers should be able to precisely estimate effective parameters on seismic behaviour, to utilize them in mathematical modelling of structures for increasing their reliability. This aim can be achieved by destructive or non‐destructive tests (NDT). In this paper, NDTs for estimating static and dynamic characteristics of reinforced concrete structures are reviewed. Finally, two essential dynamic NDTs, forced vibration method and ambient vibration method, are discussed in detail, and are proposed for seismic assessment in all kinds of structures. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling urban growth using video prediction technology: A time‐dependent convolutional encoder–decoder architecture

This paper presents a novel methodology for urban growth prediction using a machine learning approach. The methodology treats successive historical satellite images of an urban area as a video for which future frames are predicted. It adopts a time‐dependent convolutional encoder–decoder architecture. The methodology's input includes a satellite image for the base year and the prediction horizon. It constructs an image that predicts the growth of the urban area for any given target year within the specified horizon. A sensitivity analysis is performed to determine the best combination of parameters to achieve the highest prediction performance. As a case study, the methodology is applied to predict the urban growth pattern for the Dallas–Fort Worth area in Texas, with focus on two of its counties that observed significant growth over the past decade. The methodology is shown to produce results that are consistent with other growth prediction studies conducted for the areas.     

Performance evaluation of special and intermediate moment‐resisting reinforced concrete frames using pushover and incremental dynamic analysis

In this study, the seismic performance of special and intermediate moment‐resisting reinforced concrete frames are evaluated through nonlinear static and dynamic analysis. According to experimental studies, one of the most important parameters affecting the behavior of special and intermediate ductile reinforced concrete frames is the transverse reinforcement ratio. In this paper, constitutive law of material for concrete under the influence of various transverse reinforcement ratios have been derived using Mander et al. model, and 20 ground‐motion accelerograms have been utilized for dynamic analysis. Additionally, the results of pushover and incremental dynamic analysis were compared in order to evaluate seismic performance of the selected high‐rise structures. Results reveal that both types of reinforced concrete frames with beam‐hinge type failure mechanisms have ductile behavior. Special moment frames have higher ductility because of early entry into nonlinear range resulting in higher plastic rotations, which result in greater lateral displacements. Due to the differences in behavior of intermediate and special ductility frames, confinement has an important role in the ductile behavior of structures. Copyright © 2011 John Wiley & Sons, Ltd.     

‘Eco-civic’ optimisation: A nested framework for planning and managing landscapes

An important institution for regional resource governance is civic engagement in local affairs, including resource use issues. Local civic engagement has traditionally been structured around local government and, more recently, to catchment-based decision-making bodies. If citizens are to participate in regional resource management in ways that are meaningful to them, it is important that both the landscape units being discussed and the jurisdictional boundaries are meaningful. We have been examining how boundaries for resource management regions might be identified. Three considerations are believed to be important if regional resource management is to be meaningful to the citizens involved. Firstly, that the regional boundaries maximise the areal proportion of the region that residents consider to be part of their ‘community’, which should lead to greater commitment to civic engagement in resource management. Secondly, that the character of the landscape units within the region possess a high degree of homogeneity, reflecting greater coincidence of interest among the inhabitants of the region. The third consideration is a hierarchical multi-scaling capacity to deal with externalities of resource use. The approach was tested through identification of a series of nested ‘eco-civic’ resource management regions for north-eastern New South Wales in Australia. The results delineate resource governance regions that nest at local to regional scales for integrated natural resource management. Such ‘eco-civic’ regions demonstrate a better spatial representation of social and ecological characteristics than existing regional frameworks.     

Cost estimation of structural skeleton using an interactive automation algorithm: A conceptual approach

This paper addresses a new concept for generating bills of quantities (B.o.Qs) using AutoCAD drawings for a building project, which demonstrates the application of Industry Foundation Class (IFC), developed by International Alliance for Interoperability (IAI). The procedure considers types of materials and the structural shapes of the AutoCAD drawings to compute the cost of the structural skeleton elements using interactive automation. The main concept focuses on using layer computation of the AutoCAD drawing after converting it into a drawing inter-exchangeable file format (DXF). Once the coordinates are detected, it is easier to determine the area and volume for any structural shape, including circles and polygons. The extracting method is a new technique for structural engineers and quantity surveyors to estimate required material for beam, columns, slabs and foundations. The algorithm extracts and recognizes the layers and objects from a two dimensional DXF drawing along with the coordinates information. The results obtained using this technique are more accurate and reliable than manual procedures or any other traditional techniques. In this paper, an automated and interactive procedure for B.o.Q computation is demonstrated. The process involves a user-friendly interface, dynamic linking to the structural drawings and tracking of B.o.Q modifications at the same time.     

Cost estimation of structural skeleton using an interactive automation algorithm: A conceptual approach

This paper addresses a new concept for generating bills of quantities (B.o.Qs) using AutoCAD drawings for a building project, which demonstrates the application of Industry Foundation Class (IFC), developed by International Alliance for Interoperability (IAI). The procedure considers types of materials and the structural shapes of the AutoCAD drawings to compute the cost of the structural skeleton elements using interactive automation. The main concept focuses on using layer computation of the AutoCAD drawing after converting it into a drawing inter-exchangeable file format (DXF). Once the coordinates are detected, it is easier to determine the area and volume for any structural shape, including circles and polygons. The extracting method is a new technique for structural engineers and quantity surveyors to estimate required material for beam, columns, slabs and foundations. The algorithm extracts and recognizes the layers and objects from a two dimensional DXF drawing along with the coordinates information. The results obtained using this technique are more accurate and reliable than manual procedures or any other traditional techniques. In this paper, an automated and interactive procedure for B.o.Q computation is demonstrated. The process involves a user-friendly interface, dynamic linking to the structural drawings and tracking of B.o.Q modifications at the same time.     

Postdisaster image‐based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks

Reinforced concrete (RC) buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolutional neural networks have been adopted in recent research to rapidly quantify the damage state (DS) of structures. In this article, an advanced object detection neural network, named YOLOv2, is implemented, which achieves 98.2% and 84.5% average precision in training and testing, respectively. The proposed YOLOv2 is used in combination with the classification neural network, which improves the identification accuracy for critical DS of RC structures by 7.5%. The improved classification procedures allow engineers to rapidly and more accurately quantify the DSs of the structure, and also localize the critical damage features. The identified DS can then be integrated with the state‐of‐the‐art performance evaluation framework to quantify the financial losses of critical RC buildings. The results can be used by the building owners and decision makers to make informed risk management decisions immediately after the strong earthquake shaking. Hence, resources can be allocated rapidly to improve the resiliency of the community.     

A time series model for influent temperature estimation: application to dynamic temperature modelling of an aerated lagoon

Thirty-nine linear regression and time series models were built and calibrated for influent temperature (Ti) estimation at the primary aerated facultative lagoon in a municipal wastewater treatment plant. The models were based on mean daily ambient air temperature (Ta) and/or daily rainfall (P), and-optionally-wastewater temperature autoregression. The best fits were achieved with some time series models involving Ta and P, and Ti autoregression. The best-fit model was able to estimate influent temperature with a root-mean-square-error of 0.5 degrees C, and an R2 of 0.925, for the calibration period of 10.5 months. In addition, a dynamic lagoon-temperature (Tw) model from the literature was modified in its terms of solar radiation and aeration latent heat, and applied to the primary lagoon. The model was fed with the estimated influent temperature, and five model parameters were identified by calibration against 10.5-month Tw data. Dynamic lagoon-temperature estimation results were comparable to or better than other results of long-term simulations found in the literature. Sensitivity analyses were run on both models. Further validation with independent sets of data is needed for verification of the predictive capability of the models.     

Non‐linearities in regional growth: A non‐parametric approach

This paper analyses the determinants of regional economic growth in the European Union adopting a non‐parametric approach. Although the local‐linear kernel estimator applied does not explicitly take into account the spatial dimension of the data, it is found to be consistent in our context. In addition, the geographically weighted regression turns out to be less efficient. We obtain evidence of a non‐linear relationship between regional growth and its determinants in the form of parameter heterogeneity and threshold effects. These non‐linearities mainly affect the initial productivity of labour, the human capital endowment and, as a novelty, the level of infrastructures.     

Outcome‐based ventilation: A framework for assessing performance,health, and energy impacts to inform office building ventilation decisions

This article presents an outcome‐based ventilation (OBV) framework, which combines competing ventilation impacts into a monetized loss function ($/occ/h) used to inform ventilation rate decisions. The OBV framework, developed for U.S. offices, considers six outcomes of increasing ventilation: profitable outcomes realized from improvements in occupant work performance and sick leave absenteeism; health outcomes from occupant exposure to outdoor fine particles and ozone; and energy outcomes from electricity and natural gas usage. We used the literature to set low, medium, and high reference values for OBV loss function parameters, and evaluated the framework and outcome‐based ventilation rates using a simulated U.S. office stock dataset and a case study in New York City. With parameters for all outcomes set at medium values derived from literature‐based central estimates, higher ventilation rates’ profitable benefits dominated negative health and energy impacts, and the OBV framework suggested ventilation should be ≥45 L/s/occ, much higher than the baseline ~8.5 L/s/occ rate prescribed by ASHRAE 62.1. Only when combining very low parameter estimates for profitable impacts with very high ones for health and energy impacts were all outcomes on the same order. Even then, however, outcome‐based ventilation rates were often twice the baseline rate or more.