Department of Civil & Environmental Engineering

Civil & Environmental Engineering: building skills to build a better world.

Civil and environmental engineering is the engineering of constructed facilities:

buildings, bridges, tunnels, and dams
harbors and airports
waterways, railways, and highways
water power, irrigation, drainage, and water supply
wastewater and hazardous waste disposal
environmental health systems

Civil engineers are the professionals who plan, design, direct the construction, and oversee the maintenance of these facilities.

Academic programs

BS in Civil Engineering
BS in Civil Engineering - Environmental Resources Engineering Concentration
BS/MS in Civil & Environmental Engineering - Accelerated Program
MS in Civil & Environmental Engineering

Students who study civil engineering may be interested in pursuing advanced degrees, including UMassD's PhD in Engineering & Applied Science.

Excellence in civil engineering

The department’s faculty members are excellent teachers, active researchers, and recognized innovators, with a focus on:

coastal engineering
environmental engineering
geotechnical engineering
structural engineering
transportation engineering
water resources engineering

Our up-to-date classrooms, laboratories, and facilities make cutting-edge learning and discovery possible—with research opportunities available at both the undergraduate and graduate levels.

Civil & environmental engineering at UMassD

Accreditation
Educational objectives
Learning outcomes
Research

Department News

News

Dylan Cantara '24 and Christian Fall '24 win the 2023 Corsair Idea Challenge
$1,000 of cash prizes awarded in eighth annual pitch competition
Hangjian Ling receives NSF CAREER award
The $505K award provides funding for research and student training in hydrodynamics and marine technology.
College of Engineering adjunct faculty finds new evidence for ringing black holes
A study led by Collin Capano, adjunct faculty in the Department of Physics, has been published in Physical Review Letters.

Contact

Department of Civil & Environmental Engineering

Violette, Room 110
UMass Dartmouth
285 Old Westport Road
Dartmouth, MA 02747-2300

508-999-8464
508-999-8964
memanuello@umassd.edu

Chairperson

Daniel MacDonald, PhD

Professor
SMAST / Estuarine & Ocean Sciences
School for Marine Science & Technology East, New Bedford 233

508-910-6334
dmacdonald@umassd.edu

Daniel MacDonald, PhD

Professor / Co-Chairperson
Civil & Environmental Engineering
Violette Research 107B

508-910-6334
dmacdonald@umassd.edu

Jonathan Mellor, PhD

Assistant Teaching Professor / Co-Chairperson
Civil & Environmental Engineering
Violette Research 108B

508-999-8464
jmellor@umassd.edu

Events

Dec 11 9:30AM

EAS PhD Dissertation Defense by Shayan Razi
EAS PhD Dissertation Defense by Shayan Razi Date: December 11, 2023 Time: 9:30am Topic: An Energy-Based Lattice Element Approach to Modeling Linear and Nonlinear Response of Complex Building Systems Location: CSCDR TXT 105 Abstract: Extreme loading conditions due to natural hazards such as windstorms, earthquakes, and floods can lead to the failure of both structural and non-structural elements, and subsequently, damage to the entire structural system. The significant economic repercussions of such events call for the revisiting of engineering approaches to resilience assessment towards the examination of the functional integrity of civil infrastructure. This assessment requires the development of accurate yet computationally efficient frameworks to model the failure of systems comprising structural and non-structural components. To this end, we leverage a Potential-of-Mean-Force (PMF) approach to Lattice Element Method (LEM), a class of discrete methods demonstrated to be particularly advantageous for simulating failure and fracture, to capture the mechanical response of structural systems in both linear and nonlinear regimes. The premise of the proposed framework is to discretize the system into a set of particles that interact with each other through prescribed potential functions, which represent the mechanical properties of different types of members. Lending itself to damage assessment due to its discrete nature, our PMF-based LEM transcends the limitations of continuum mechanics approaches and enables the incorporation of a range of effective interaction potentials, to simulate the linear and nonlinear behavior of structural components. Since the determination of elastic behavior is a precursor to the failure analysis of structural components, harmonic potentials are initially adopted to model the linear response of various structural members, e.g., beams, columns, roofs, and walls. The calibration procedure for such potentials is thus carried out via a handshake with continuum mechanics theories, e.g., the Timoshenko beam theory and Kirchhoff-Love plate theory. This calibration is then carried out for non-harmonic potentials by adopting section properties that encapsulate the nonlinear stress-strain responses of the materials, e.g., nonlinear moment-curvature relations. Upon the calibration of non-harmonic potentials, ductile failure of the structural members is modeled by breaking bonds between particles according to an energy-based failure criterion. Finally, the utility and accuracy of the proposed framework is demonstrated through its application in (i) both quasi-static linear and nonlinear simulations of large-scale buildings under different loading conditions, (ii) the simulation of progressive structural failure due to the propagation of local structural damage. ADVISOR(S): Dr. Mazdak Tootkaboni, Dept of Civil & Environmental Engineering (mtootkaboni@umassd.edu) COMMITTEE MEMBERS: Dr. Arghavan Louhghalam, Dept of Civil & Environmental Engineering Dr. Yanlai Cheng, Department of Mathematics Dr. Alfa Heryudono, Department of Mathematics NOTE: All EAS Students are ENCOURAGED to attend.
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Dec 12 10:30AM

ELEC Doctor of Philosophy Dissertation Defense by Christian C. Ellis-ECE
Topic: Terrain Aware Autonomous Ground Navigation in Unstructured Environments Informed by Human Demonstrations Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/3753158123 Meeting ID: 375 315 8123 Passcode: 495427 Abstract: Mobile robots equipped with the capability to perform autonomous waypoint navigation can replace humans for applications such as humanitarian assistance, nuclear cleanup, and reconnaissance. In such tasks, the robot must be able to accurately and reliably perform complex behaviors such as the ability to navigate over unstructured terrain and respond to unseen situations similar to how a human would. However, to implement complex behaviors beyond obstacle avoidance, many current approaches employ machine learning methods requiring large amounts of labeled data. While simulations can quickly generate large amounts of labeled data, the same cannot be said for real world environments, limiting adoption of mobile robots to complete the aforementioned applications. Moreover, solutions are often brittle, exhibiting poor performance when operating outside of environments beyond where they were designed or trained. Therefore, there is a need for methods which can quickly learn navigation behaviors from limited data while being able to adapt to dynamic scenarios. To communicate both positive and negative environmental scenarios, roboticists assign rewards (or inversely, costs) to all the relevant environmental features expected. For robots that need to quickly transition between varying unstructured environments, defining rewards prior to understanding all future features the robot will encounter is often unachievable as either: (i) the robot is unable to perceive the new feature, and (ii) the numerical reward for each feature is unknown. In such scenarios, it is more effective for a human supervisor to provide examples of desired behavior than for an engineer to explicitly define it. Therefore, this dissertation focuses on a ground robot's ability to incorporate human demonstrations as a weak supervisory signal to solve each respective preceding problem by (i) obtaining a semantic perception model capable of classifying terrains present in the current environment given a sequence of unlabeled (unsupervised) images and (ii) using Bayesian inverse reinforcement learning to learn rewards associated with the terrains identified to build cost-maps online for autonomous waypoint traversal. Advisor(s): Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Jiawei Yuan, Associate Professor, Department of Computer Information Science, UMASS Dartmouth; Dr. Craig T. Lennon and Dr. Maggie B. Wigness, United States Army Research Laboratory NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Lance Fiondella at 508.999.8596 or via email at lfiondella@umassd.edu.
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Dec 12 11:00AM

EAS Doctoral Proposal Defense by David Gillcrest
EAS Doctoral Proposal Defense by David Gillcrest Date: Tuesday, December 12, 2023 Time: 11:00 a.m. Topic: Inverse Analysis and Calibration of Physical Models: An Approached Based on Greedy Kaczmarz Multi-Fidelity Surrogates and Aggregated Directional Statistics Location: CSCDR - TXT 105 Abstract: Advances in surrogate modeling have allowed for highly accurate Polynomial Chaos (PC) expansions to be constructed for physical models ranging from mild to major complexity in their respective parameter spaces. Multi-fidelity techniques in the field of uncertainty analysis have been employed to efficiently capture the variations in the outputs of models given reasonable perturbations in these parameters. Recently, a greedy Kaczmarz algorithm (GKA) has been used to cheaply construct surrogate models in a way that greatly outperforms the previous least square approaches that demand samples about 2-3 times the number of coefficients in the PC expansion. In this research we look at the utility of GKA-produced surrogate models in solving parameter estimation problems - inverse problems typically solved using statistical least squares methods - for partial differential equations (PDEs). We present a generalized approach to solving two-dimensional parameter estimations for PDEs and demonstrate its potential using two Advection-Diffusion-Reaction equations: one for the source localization of a river contaminant, and the other for the estimations of both the area source contamination magnitude as well as ambient pollution concentration in the context of an urban heat island under the influence of mesoscale wind. The physical domain of each problem is discretized in such a way as to accommodate the hypothetical usage of detection apparatuses. An array of surrogate models is constructed to capture the variation in outputs at these detectors with respect to the PDEs' parameter spaces. Using directional statistical techniques we examine the optimal combination of the surrogate models, specifically by looking at the local interactions of each model's slope field. The quality of the combination of surrogates can then be quantified and aggregated in order to achieve a cost value for a grouping of surrogates. ADVISOR(S): Dr. Mazdak Tootkaboni, Dept of Civil and Environmental Engineering (mtootkaboni@umassd.edu) Dr. Yanlai Chen, Dept. of Mathematics (ychen@umassd.edu) COMMITTEE MEMBERS: Dr. Sigal Gottlieb, Department of Mathematics Dr. Zheng Chen, Department of Mathematics NOTE: All EAS Students are ENCOURAGED to attend.
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Dec 13 9:30AM

EAS Doctoral Proposal Defense by Soolmaz Khoshkalam
EAS Doctoral Proposal Defense by Soolmaz Khoshkalam Date: Wednesday December 13, 2023 Time: 9:30am Topic: A Dynamic Formulation for Potential of Mean Force-Based Lattice Element Location: CSCDR TXT 105 Abstract: The potential-of-mean-force (PMF) approach to the lattice element method (LEM) has recently been developed and utilized for simulating the fracture in heterogeneous materials and adapted to model the response of structural systems. LEM is a quasi-static approach which relies on lattice discretization of the domain via a set of particles that interact through prescribed potential functions, representing the mechanical properties of members. The approach offers unique advantages, including robustness to discontinuity and failure without the need for mesh refinement, and the ability to accurately simulate nonlinearities through the use of non-harmonic potentials. The overall goal of this research is to extend the PMF-based LEM for simulation of dynamic response. Such simulation framework provides a means for simulating nonlinear response and failure under dynamic loading that is the nature of most natural hazards and extreme conditions. Furthermore, a dynamic LEM approach opens the door for consideration of dynamic fracture and wave propagation in heterogeneous media. For our analysis we leverage the advances in Molecular Dynamics (MD) integration methods for estimation of the trajectory of particles in the Lattice Element Method (LEM) and to simulate the dynamic response with a focus on structural (or building) systems. To this end we use Verlet-Velocity method for time integration, to estimate the location and momentum of each particle at every time step. To address the limitations of the explicit integration methods regarding small time-increments and to assure accuracy and the numerical stability, we also plan to explore implicit integration techniques such as Hilber-Hughes-Taylor method and midpoint method. Noting that the rotational degrees of freedom have minimal contribution to the kinetic energy of the system we develop an energy-based approach for static condensation to reduce the computational cost. Our approach relies on the Euler-Lagrange equation which manifests in the form of minimum potential energy theorem for mass-less degrees of freedom. Finally, shifting attention to another critical aspect of dynamic simulation the mass matrix, we adopt an energy-based approach and utilize the kinetic energy of the lattice elements to maintain consistency with the kinetic energy of their continuous bar counterpart. ADVISOR(S): Dr. Mazdak Tootkaboni, Dept of Civil and Environmental Engineering (Advisor (mtootkaboni@umassd.edu) Dr. Arghavan Louhghalam, Dept. of Civil and Environmental Engineering (Co Advisor)(Arghavan_Louhghalam@uml.edu) COMMITTEE MEMBERS: Dr. Alfa Heryudono, Department of Mathematics Dr. Zheng Chen, Department of Mathematics NOTE: All EAS Students are ENCOURAGED to attend.
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Dec 13 1:00PM

Mechanical Engineering MS Thesis Defense by Mr. Ali Nosrati (New Date and New Time)
Mechanical Engineering MS Thesis Defense by Mr. Ali Nosrati New DATE: December 13, 2023 New TIME: 1:00 p.m. - 3:00 p.m. LOCATION: Science & Engineering (SENG) Building, Room 110 and Zoom link: https://umassd.zoom.us/j/94943769488?pwd=a0E0eHJLRUdTbnBrT1J5YklxaGZJQT09 (contact hling1@umassd.edu or scunha@umassd.edu for Passcode) TOPIC: An Experimental Study of the Longevity of Super-Hydrophobic Surfaces in Undersaturated Liquid Due to Gas Diffusion ABSTRACT: Superhydrophobic surfaces (SHS), created based on a combination of surface texture and hydrophobic chemistry, have a variety of applications from reducing drag to protecting underwater surfaces from icing, corrosion, and biofouling. These applications mainly rely on the presence of micro/nano-scale gas bubbles trapped within the surface texture when the SHS contacts with the liquid. However, the gas on the SHS can be slowly dissolved by the surrounding liquid if the liquid is undersaturated with gas. Once all the gas is dissolved, the SHS is a purely rough surface and loses most of the aforementioned benefits. This thesis aims to understand the longevity of SHS in undersaturated liquid due to the effect of gas diffusion. First, we developed an experimental method which improved the measurement accuracy of SHS longevity compared to prior works. The higher measurement accuracy in our experiments was achieved for three reasons: (i) to measure the status of gas on SHS, we applied a non-intrusive optical method that did not disturb the gas diffusion process; (ii) to address the uneven gas diffusion at different regions of the SHS, we measured the SHS longevity based on the gas status at the entire SHS sample; and (iii) we induced the gas diffusion by using liquid with a low dissolved gas concentration so that the stability of SHS was not affected by pressure. Second, we studied the influence of the undersaturation level of the liquid on the SHS longevity, which is an open question in the literature. We defined the undersaturation level of liquid s as the ratio of the gas concentration in the liquid to the gas concentration at the gas-liquid interface. We found that the SHS longevity tf and the undersaturation level s followed a power-law relation: tf~(1-s)-2, which is in good agreement with a previous numerical model. This scaling relation suggested that as gas slowly dissolves into the liquid, the gas concentration in liquid near the SHS increases, and the mass flux of gas from SHS to the liquid decreases. We also found that the diffusion length, representing the height of the liquid affected by gas dissolution, was inversely proportional to the undersaturation level. Lastly, we studied the influences of SHS texture height, chamber height, and surfactant on the SHS longevity. Overall, this study improved our understanding of SHS longevity in undersaturated liquids and could guide the applications of SHS for reducing icing, corrosion, and biofouling. ADVISORS: - Dr. Hangjian Ling, Assistant Professor, Department of Mechanical Engineering, UMass Dartmouth - Dr. Mehdi Raessi, Professor, Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBER: - Dr. Alex Fowler, Professor, Department of Mechanical Engineering, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Hangjian Ling (hling1@umassd.edu). Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Hangjian Ling (hling1@umassd.edu) or Sue Cunha (scunha@umassd.edu) for more information, or the Passcode).
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